{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "801dbe59",
   "metadata": {},
   "source": [
    "### 11.2.3 对乳腺癌数据集进行前馈神经网络分类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed02d0a9",
   "metadata": {},
   "source": [
    "#### 1、\t导入必要的库："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "afe52566",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "405b55c2",
   "metadata": {},
   "source": [
    "#### 2、 加载数据集："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b97c3071",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('data/breast_cancer_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db6e8bdd",
   "metadata": {},
   "source": [
    "#### 3、准备数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d06e8ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.drop(['id', 'diagnosis', 'Unnamed: 32'], axis=1) # 特征\n",
    "y = dataset['diagnosis'] #标签\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "089166be",
   "metadata": {},
   "source": [
    "#### 4、创建并训练模型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2028fe77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(max_iter=500, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(max_iter=500, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(max_iter=500, random_state=42)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建MLPClassifier模型\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(100, ), max_iter=500, activation='relu', solver='adam', random_state=42)\n",
    "# 训练模型\n",
    "mlp.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ea345a5",
   "metadata": {},
   "source": [
    "#### 5、模型评估："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af6d0b70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9649122807017544\n",
      "Confusion Matrix:\n",
      "[[69  2]\n",
      " [ 2 41]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           B       0.97      0.97      0.97        71\n",
      "           M       0.95      0.95      0.95        43\n",
      "\n",
      "    accuracy                           0.96       114\n",
      "   macro avg       0.96      0.96      0.96       114\n",
      "weighted avg       0.96      0.96      0.96       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 在测试集上进行预测\n",
    "y_pred = mlp.predict(X_test_scaled)\n",
    "# 计算准确率\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy}')\n",
    "# 打印混淆矩阵和分类报告\n",
    "print('Confusion Matrix:')\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print('\\nClassification Report:')\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e6d35ed",
   "metadata": {},
   "source": [
    "### 11.2.4使用网格搜索寻找最优前馈神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2485ce9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72dd0d75",
   "metadata": {},
   "source": [
    "#### 定义参数网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "06a0e4f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'hidden_layer_sizes': [(50, ), (100, ), (50, 50), (100, 50)],\n",
    "    'activation': ['relu', 'tanh'],\n",
    "    'solver': ['adam', 'sgd'],\n",
    "    'max_iter': [200, 500, 1000],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a7f703b",
   "metadata": {},
   "source": [
    "#### 创建模型和GridSearchCV对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4b4ee6e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp = MLPClassifier(random_state=42)\n",
    "grid_search = GridSearchCV(mlp, param_grid, cv=5, scoring='accuracy', n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a74566ad",
   "metadata": {},
   "source": [
    "#### 执行网格搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "91d41a7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=MLPClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={&#x27;activation&#x27;: [&#x27;relu&#x27;, &#x27;tanh&#x27;],\n",
       "                         &#x27;hidden_layer_sizes&#x27;: [(50,), (100,), (50, 50),\n",
       "                                                (100, 50)],\n",
       "                         &#x27;max_iter&#x27;: [200, 500, 1000],\n",
       "                         &#x27;solver&#x27;: [&#x27;adam&#x27;, &#x27;sgd&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=MLPClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={&#x27;activation&#x27;: [&#x27;relu&#x27;, &#x27;tanh&#x27;],\n",
       "                         &#x27;hidden_layer_sizes&#x27;: [(50,), (100,), (50, 50),\n",
       "                                                (100, 50)],\n",
       "                         &#x27;max_iter&#x27;: [200, 500, 1000],\n",
       "                         &#x27;solver&#x27;: [&#x27;adam&#x27;, &#x27;sgd&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=MLPClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={'activation': ['relu', 'tanh'],\n",
       "                         'hidden_layer_sizes': [(50,), (100,), (50, 50),\n",
       "                                                (100, 50)],\n",
       "                         'max_iter': [200, 500, 1000],\n",
       "                         'solver': ['adam', 'sgd']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0412a438",
   "metadata": {},
   "source": [
    "#### 获取最佳参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eec96fe3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'activation': 'tanh', 'hidden_layer_sizes': (50, 50), 'max_iter': 200, 'solver': 'sgd'}\n"
     ]
    }
   ],
   "source": [
    "best_params = grid_search.best_params_\n",
    "print(f'Best Parameters: {best_params}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5410401",
   "metadata": {},
   "source": [
    "#### 评估最佳模型性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c306de14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model Accuracy: 0.9649122807017544\n"
     ]
    }
   ],
   "source": [
    "best_model = grid_search.best_estimator_\n",
    "y_pred = best_model.predict(X_test_scaled)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Best Model Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2c315f8",
   "metadata": {},
   "source": [
    "### 11.3.3 卷积层TensorFlow实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d8e5486f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([175, 287, 3])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "car = tf.io.read_file('data/car.jpg') # 读取本地图像\n",
    "car = tf.image.decode_jpeg(car,channels=3) # 将JPEG编码图像解码为unit8张量\n",
    "car.shape # 查看car的形状"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8292b85a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2cf5a01b2b0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(car) # 绘制汽车图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "95ceadc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 一层卷积网络\n",
    "from tensorflow.keras import Sequential,layers\n",
    "model = Sequential()\n",
    "model.add(layers.Conv2D(3,(3,3),input_shape=car.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4850cf79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据处理后的形状： (1, 175, 287, 3)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "car_batch = np.expand_dims(car,axis=0)\n",
    "print('数据处理后的形状：',car_batch.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac8c0c61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "查看进行卷积运算后的形状： (1, 173, 285, 3)\n"
     ]
    }
   ],
   "source": [
    "conv_car = model.predict(car_batch)\n",
    "print('查看进行卷积运算后的形状：',conv_car.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f4082e8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "查看特征图最小值： -196.80255\n",
      "转换后的形状： (173, 285, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_car(car_batch):\n",
    "    print('查看特征图最小值：',car_batch.min())\n",
    "    car = np.squeeze(car_batch,axis=0)\n",
    "    print('转换后的形状：',car.shape)\n",
    "    plt.imshow(car)   \n",
    "visualize_car(conv_car)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "385a0971",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "查看特征图最小值： 0.0\n",
      "转换后的形状： (173, 285, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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xXyOXhB2/HJtpraeEM1jL6tquWAxl0B8zRkabkQajMba8sZVALNKyqIf5n1kJrCk3vxGY0pgVJeXDQM8Ix4cRUNAAG7DlvKWYEr0aOAxjrJzjplkHPMZjDOVX5vIet2EvszfcvzdjhuImwBHEM4wDgasYwbksYYmby1ySOZLPgWMItEEBPxDHGbzu/j7RrVMw/GV1B5KiN8kOi5hyjwEw726TsbXMm7CZ2QjgdOxT+EPs8z6Gukc3/KGlbdZ5J7Z8EA3/AyZUU9n7YeHvVmJK/Liw88dis3v/6r2DLa1cia2zh2OQ+/cowEcv3mMfbFXeXkp/MoV1LGE2MM7Nby9sRu/3ZJkPLMTHD6wCfmALWziWY9mDPVjJSl7kWX7Agn4DHAH0Zw0ZrOEA4A6gMELd5N7Hz9hXxBHAl+U3zw6FmHL/l2MgA5lLCn8Ac9jEoUykL3Ao9jlciLmGfba8TGIoRSLGrP6W6J/724sthM4y7yOy4gSLpDMByvni6gN0xFTlD1FTxWEz7RuwJbl5QBegb1CaQdga90uYm9hE91hr7IVUnrJZBYyhJ89zPn7l7gFm8Tor3TT7YXz9e8OuzQF+xMOPNONgppBPPu1pT99WfVnTej4LJsAfmAMssADse7l1/QY7F20h6j737/XAQKzdJ5ZzHzsU6poGGaNC1q38zGydidScXA3mC80GNcfiUi4Hnb4D1PGfJM1BSzFvg42J7GkweHdoMtEjUSW6+QXv9k13jw0BrcTCFLal/DB5n4POjnr+a4EEY6JeHw/qiI2HEkssYfFIg+s3HbRPWFssd6V1pdrvJMG4kGPjydQmmms56fqt3GszBMuV12y5BjbvoObJqPkwdPA3VuflYfKc22atQctATSpRvxGgL2po3GyHxNwPxCSazNbbSCU8oQkYD7mE6AGGd0ZxqiCVzQ/QPNB/wo6F/38maFqEeoBtxy/BHH35lfdn7rFx2EuhxJUuUfKPVK+AOIJvRAXKvS9og/v/xqAy/bKlhvoD0I+M13+R4GM5rpfH0nROkGC8+LwtSCWoZFigfpuilNMBlFeFfg0pdxuuqyGJKfeYRJPZikNK4AntDVoDSqjzOtWeNAXlV1K+p+JYny0xZReHzXg9oCswZexP8zboEFCGez4em8EHl3Wley4hqD8uxzbWJLj5E3Ter2jOAU2MUK+HQW+WOb5ItmmpfOXuBNUhIYpUV3/sBtoK+gZUHxTHeHmQjqZYs5kdkvajj1B+PsofE2g3XwJSAhruqVz9qlr3PfdE+autrOWYn586HsMx5R6TcHEEfwkK9AhX6GaaKAmbzdR93WpPvKBOFUgutgyxFVsK+bKc/OLca4KPpWOf/w1As7BdmCmEBthwwspMj5B3OvYiCD8+jUAglPOw2eisIOmJLRE1L3PtcoF0EV/oZe6XBfeo2/5IoKM68adalbZPa0EnDaWTCmkXcl+bW6LnOqFOrcr2WQPQSWHtUL8a6peU1EN99vhDvllo9xQ0GnRy3bZZzTgOcxynIeZSpLtb0AhsX8C7GL12EXCipOztKSeG6kYa5j6qK3AtH/Ix+awhn+rd6FKTeAVjU/ixCKPGVRUllBdmzjC8tKweQBw5TI2YridwTdixx7CdjtnYTtE7gcVuuWBGwvOqXOtQPIiFikvFNtwkYRt0zgaeBx4AHiU4UJ4XeBWjP97PeEYzi10wgmU4DqE9A7ie27a7npHwEmZ4fQFjphSyhkXcx8ulKZYCFq9zI9Z+pVgOf2OG18uBCzHz8JmYb5olYenzq6G++fkrmP73SIbdCVmF8BT2zAzAqJbXVkMZ1YXtZcs8Dnwt6XjHcRKAFOBGYKyk+x3HuR4zNF+3neXEUG1oRTKncgan8AIv4ONNfmEN3YELMHaM/8E6jgCXOpvti1B/NvAxZbnEfnRzy1gZ5bwfScBZGCd6LMaUSIfS+lcH/gJ+CfodcLRVXCZtMEoo626rGNsW1A7bBPM2oW1RFOGacHgwhkek7eTC2BtHAzOBKa74sMikDsauCaX6eYBT3b/fM4N8jD8SCZ1oxBkMYzFTeJmXKG+LU1k45dQdbKfpMSeALxO6zgJ+2EQ873Iq9vrxK+Qs4Ces/cB48+MxBd4R27UK1t65mGKbEZS++rCeoqL3edvN+Gv3aAbl7SOoGzjuskjVL3ScNOBPoL2CMnEcZzZwgKSVjuM0B36QVN7uaBzH2bZK/GvRBnt8l5abysFmk4FdjM2Ag2jAVTyM6E9/iimmLabIT8Uetkvd1G9h21vWYTNjP6e5CzbbWkV5FLtQ5GIzYP+XQR62EcaP47BgzQswRVhC5JlHKjYbnotRNVe46Y/APhurA+9S1rVCZ2zmsi0Y1gQOTIMRrm/bZ7D9A8FfSWX7KgAvpsC9Ec4Jo/T5sF2oX7jHkzF+NthMdkbIVfGY2vRgpNe9sDnuSqBFWAknsRt38BYt2J39Gcg0puBz3eeWRQds274fHrfucVjfbgxLPwB47V3o0AH4DCOduxiEvfhaAE2xl8p8bOx8idEUf4pSj9pGOtCWRHx0YRrTarPoyZL2jHhmO9bJ98C8Z76Kecp8EdMFG4PSOMG/Y2vuVZEkQaor9dxj9WRr5f9TcPizVFJd8SgVSiUdYwrkuJLPdRKfamVQGgf0Cugx93cLbH15s3vNRe7xlKC6TcSMhndEOBdNVgXVI4fIRj9AR2KMjCVh6f2yMMI1XswgmVqDMtVtl6rKVlDOpShnLMpJNWkcIf/GGK0x+Noitx/y3HO5Udqk6hGv4pVKiVLZrFQOVCrXKZUcpTK3TL0SQR76qQWS8Emkat9klJrqSkpo+h+D6h5ez6PC6hG5rMBvf7q73OvngPao8r3WjpwEyqWz8shTfeqXYfbUoFS/QRXz8VMM7OX+fhzzir8xLF12lOvPw5YjJ9V1x+yY8rkshqVPsMY9tlXQJSRdKqny4XNlL/mgVMJjZd6CPXjBaTq65y4JO+43Pj3j/p4ZVr9xQWmX1HFbHQ76Oaz+NSHaBvFTIRmA8JnkplQu/49A9UDDQMVUlitescQTrxJK5KN5hff8BKgf/eQLUu6+Scjnc2Vm5PsYXUEd0iOU9TKoe9DvjFoYO9UpneksIfnwqTGNa6vcGjGoLgOWSfrN/f0Btr6+2nGc5kHLMhGdVUh6HrP3/MuXZe4A2hPY6L/K/XsOcDoDgM/JwFZoy3q4cMjFIYPjgN/ZHBIsIx7blu5f79wCvI4Zn/wYi7kaAFur9W9r34wFd/gAW08swQyCK7EliqMJLJv4tuGuuxK6rr09iMdaprpds27CWAHbgy3AxcBdE7GGBFK2htY1C1ueCEeRe/27mPOq6oqLVEQRmWSW5njtNXDDDWGJ9gQW2Hr5mb3B+YjSxhg0CKZ5HwaGByzDAHTC7xQgfF1+KtDmamwd743A/bfC/N6A2QXyKW2mWokDVRNwdhAnwdus3CWtchxnqeM4nSXNxnYnZ7lyJuY0+Uz+ec7UaglfYSavNphq+gAPJ/EDDfESh20o30gDIJ1cxBAOBF5hMW2BTwheF95IFqaQL8WcIIEp3Tew0GF52GfWBkLXPc/BjFLxGFsh+Nzl2LPoP5aIrS1+QUWmxYqR4ua1LRBwING33FcXStg+z4MA92C2jPQSyNlohuBwbK2gnKJqqEc4Nro5PgIcNwp+/xmuCE6wIuj/OZhB4xf4bjA8vhluJ5lPSMe4P6eU5vok0DtCeV2ApHeAQphOwPi9irKTg43beE91jcWYi4PvsZfxBRCFV1U72F62zKXAWy5TZgFmM/MA7zmOczZ2vyeWc/2/CHsCJwX93p+LSKZd6e8+ePAxgGvx4OElZjLTPVOPYm5nArtixsQ4zIgWyRnUdEJn1D+6x4KVcX3M+FZV+PPttw3X+vEulIZOeHMb8xBmSCupKGEdwcFmNg8AvxFgCOWzfQ68bsZIrJHwIBX5c4yMX4Dly2DRsnLqlruEpJ9v5q6P7iau+B7GUcgC9qE3MJg8HmFCqQ+WozGXvGBK2v9V+BGU+vZdTvU5MqtttMbWk2+JcC6fNUzkeuA++nE7aTyCmYDrBtvMlqnWSuzUyzKDsEfyQOpzaVDUoi+4h0J84AYIXkMwmW8c9iysw5Tzq5iieIHIdMIhhPK+y0NDjCdeFWwGvqviNZHwNLYU9E+HB/Nk6P8AzyLAl3ewiEIXE1C4GRin3Y9xVD1K6QtAZpRzl2JKc3uQSKQAGO2B1qTwB2/xJp9zJJ8QzwagPUtpx6dczaXkYd+iwV9T67FlnX8OdsUWwlaFHB2Mte1SoDlwG0bFjIREEskjDweHQQzih5r3pRqVLRNT7tWORIy4ZWjON8TTBcilM+v5Gj+BsSewiXtxDQ9heAlb5/oTuKwSpf6FLfKUh41s+zrmfGLh9IKRQGgUnwexF5eXsqHbcjCK4AdBxw51r69HqMJewfYveW0rGkOZgHRwMQmcSlMGsBTYjRzeoT49WM/HvME1XElbzAtmH6LvY/gnoAkjSGIJ4dOYt7C15Y8wSnAy0TntO5JyrzGXAv8u9wPeIBkokLxIXoo0nSIVU6QiXlQRZZ0YORiVL5oMIjoTw4dRziort9V5O/0zJFKfREoXqb92idDuI8u5bkRYP3bEXAz4HWHVdVvAMPXlfa0DefHKS468FMvLefJgDsty67yO1SNfE/m5CnbJ0Rhz9hYtj0QS5cMnIR3AAbVR75hvmZqVOYIiV4qVjlSET0XUlw+vzsErL56IiuJ+ylfIxURX7lMo/8UQLjuIF7sdXi6O0A+9wtIkggojpFsUod3DlfSmsP79i7Ivkfsxl7p13Ra2r8KjDBqoiCJ3XPdVEY7GYQ7QynM3/E8Sv3L/lvJf7OU9RzuSco8F69guWDgBaMuzxLlMiEnkcjz9gGxyEeJaLLpRJKQT2ap9NhWvTRey4xoV/ylojO3E8+NSjC46Oizd8xjRz49C97fC0pUQ2iepGNHkyrBjRxDYoVtE2X58gB0lko7pkBIc4oiz9b+TS+BvsQpzAbCzjMEzMd5aPnZPDtZHCe75VzHicnif76jYMcbPPwpHYy6CGmLdvyvP4OEoRjKJibzGehJZzJ0E/Hn0JjJf+iYoZcSEYwLhZp0YqgMvYi/U97C2vxZzY+DHVMx43BRTsH70da/xO9/yYeuvweiN9akPOBlTEJcBp2H9n0vANdcEyjeoZlf6jmoHW9nCcI7lmRsh+YoF8D4MGGN2huB2+SdjNXAC1nd+dMCmcI9g/oD+SYgp9yohDjOEnkAq9TkfH/A4OZSwlTdIYwbtMYPLkWFXziDgZMiPUcDCGq/zvxeDCHCuSzAa6RJMcWZj3P852EN7BWYMbe+mj8e4vX4swAxqfgqfF3spPOvmA8ZX919zhfv3ZPf4Q266f5qC8KOIYj7gYzp+DmfvCtM3Gx03l6qzfnZkbMT6sB7GXXsMGztvYay1fxJibJlKoy9Qn104m5YcThNm8gklwCCOp4RLsA0MOYQ7aTJ8QdnYjzFULzyE+jY8Bot7Cbb0MYjAJ3UrjLMM9v11H6EznSxCaXy7E3CiNhdT/t9jL/GNbpqG2M5bj5uf39HXKMw17M6ClzEfX5/UcrmJBF7Wk4kc1Lq6kIExZAZRNfZSPIl8Tx79cLiSQXzCD9tNUa0AMbbMtolH0FDQUGmsUUNy9BAbJN4JMWxuxMKQbQB9Ved1/ndJAuZkqj7mh2VDkJwfIX0DLJTgrUHp1hEIghEflF/wdd+7aZ+LkKeDOS77j5tmLYFISTGJLuUZJhuCGqahhl73f9DuGJNlAxbxqjbq2JCqspYSBOu1mhKJo3QjiTVdxxhbZtuks0ACn1bjkzhAoqzDo3Z1Xs9/r5yOKd7JlUy/3O2zO6KcPw30HdG9VkaS5qCnqNhZVkwC4hDdMViy20e+dci3b+izVkTtsXMcLJxex224djWr5UO6kRtrup4xtkzlMR7zZgzgIZF81tCEVIR/dXUMcHzQFTuak/6dFftifryDEY/5z7m8knl0wpZh/J/0TfHvEDaci+08BdsU5u6YZyBE9NK9AAse8l+q33HZzgxRdsPTaZgNw9+ObdpAdgTH8bXFzhG2wWxrRQkjoD1lx2ptI6bcyyAFqM/hfMld3IIHH/XZjAOcj/kn3gQh3hdjqFk8igW2eA2za4RjHUQNHhGOLdj691/A/7At8sF5rsecXGVga+r+c37XAodjfq39GI5506xpJ2b/BnxNaF+s2BJQ5AdgRmk/hlD5QDHbg22duG3hYM7hpdIIUXWBmHIHjN36sPv/25zMS5zNHFoyJSQG42hCneXFUDPIIFSBLsZeqrmYIt1efE1AMRSH5ZmIzeo3YZTG8PLmY64h/PiN6onNGYO9WNdjX2OPhZ1rh9FJ/Q67dozJ1cNYTSNFRJvOEWxmFnVHaf6XK/eOZNCHQaTwIRdxPO8Tx4OczSJ2xehPz9Z1Ff+F8BDqDvhFtt11aj3MS2FP9/cnlN2gFIwCbJNRH8p6v5yEzeDnuHU8ERiKOQHbFo+MMUSGgzGPjsNetgDLaMlb9OZZPgdst0kKxmqqfYpiPLYweyGmJSKHuzyGsezN3+Rj3llrHXVtTK0rg2ozmqkjd2oI0iSKBbM1hXrKw0LCPVgHdYpJ9UlrzBC2J+hc0GxXGoel87rpguWqoPTBcjLGoukI6gaa5R7fawe4351Rfi9t+0Z6mgsFC0vP/eCeu6pO6pYmkGCuoFvp8XhC/dC8DppNpp6nRU3Wp2bYMtiu6r8xavcobH2jHfa1Og9z352wIyn3eFfe4g0VUKRfkOLJLj33G6YM6mpA78wSR807w/JTGsdjYQbfitL/fmntpguWR8pJfybG2tiK0TD9x3cMJ187q4yUgxQfpNz948nvMym8nyJJ9dTFETSShb9MDTnXiVDHgHGgeO6Wl9E12T41EkO1JbbBMtn9/R5GHHgPONk99hxw4Y6i3B0s2HAJ6CQcOVyqvZA2uMp9I6hPnQ/knVf+BA2vwfxT3b5Nc/vaL8Fplrlp/LI8LG1w+gZhaf2SCxoTduzGHaB9d14ZqWOQ5ocp989Ad4P2i9JP4ZJWLXXpKyhRJOUOoeNnLGged+vef6hyX4rZv+KwpcxDMfJCnJtmb+Cbulbu+2EP8TJsptYclMSzGsFGjWGKmtJUgJpS9c0ne2DeGZeXIydvZ/0z3HyqHuW+7iUtqB0GYhzmmirLcfs2+AE7Kqwvit3+aO5Kk7A8ng1KuxLsEQmTEmyG1jxIUneAtt5Z5Wbq6y2alwk6fSLoAOwLqnklpHq8ovYT5AtGigo2KGWAGlNf9Umvyfapfp67pOWO4zyEuevIw+jfk4GNkvw7dpdRNnZBreAtzJY9BVs3GoEZap4FvLwIHMEu/ICH++nGalYTYFBcglGt1gFPEuo1MBwLMR8jieWkieSOoCrY7NY/POjwjooemNfDxljE9BHu8alUnrK4LRAWwPsDzJAKgfCfwZiEsTJ6UdYlRE8s2k44LsHWGf3wuWX5cSbGbb5tWyoeQ7l4j83UYzNrw45/jz0ThYT2Rc2jEPgfH1FI8pOYd7FxwINWn6PdVMbj30xfzIHc6bVax+1gyziOk46572iHudd4n8jxf6Ndfx4WjrBGcABX8BqfAQtYT8Br3V7ApQwkk+b8zhLeYSLrw65djEWkyaHiyEWboMZjrRTxz/K6txmjDK7HqIbbW/c2GN0wnNd8AdAo6HchFjR8Cmb8AXuxfoOxW27AXvB93XO7UnbAfoRNBsLxEaZAumMbmoJZVMMwmt7cspfFUA2YE+V4uLKPhKbYi3dkRQkrhb7AoSRyP9cwnyOAxL/hy71h06ndOTBxKJnFxdx8//2Aba5bjOmISBvgahzbsSxzAvBS0O8zsDG/QyzLLGe5ruVataNdmXOzmS0hPcETdf7JubNJJ9AA0IGg3tWUZ0/Qru7/8Vh0Kg/oZcxVgF8+j3J9MhZUogRbVpkadp1ftoDupPzt5oNBrwT93tfNL7bmvmNKB6KPi6rLJYKJSsPG0Q/YuDnqctT3u9P01gRJBQXSgQdK8fE6oPS6TME+NXWPNbLmvhc2yUnBJkSvYbEO3ifUoHpRXSl3Id3BHWXOxZR79YoHoxg2Bj0EmgZaTehD1SgoTbhTrqpIBkZVDbU/JAkay1gMgeOpmFG0nVsfvxwQpe6TQWtA91RQB2/QNWsxR1ZXVHOb1nPrXtd9G5NgOVvwlepj46gejQRxMsPqcHXosFqrV69RwWqffM26aqgTrxQQHCFYWlP1qjEq5B1YsJIZ2FdIIrb0+Du2RPk+kLhjKnefnuDxHWDA/POlJQHHTm2jpNkclOalaq/DeTL2Qk7I8cdA71RwbbOgeh1K5V48XQh1ZnVADbTpSNBHO0DfxqQ8yRH0FjwjeEOA4knUV/hUsJektAPcL7ojBEtqqh5RlbuH7YCk2yR1kdRd0umSCiQtkNRPUgdJJ0jaIdxuNAVycYhnI2YBORNz9xTD9mI59vmWgq0xRkLjoDQXAC2AFzCnTOlRrikfG4DOQb+nAU0AWxvfihm7oxuxbgA+Z1VQvb6FMF8gDdyctuJ3JncyZhguwIy2KZiruerCDGxn7I3ASdWY786InpjdpPbhHxepZc4UYYbITb9TarA7lI3M5c9aq50f/xr3A+uxh6aYZMy8VkjV3PDHUB4q8q8Sfn4NcDvmmSM4ks+BwOOYw6jeGCvlKWB/jOUyvDTlAGAh9wJH8T7wXWkpjbFAGddhATmuDin5e8wM2wS/mStQt9GY+fZtLNxGLn7z63vMpyu2LX4lxqaqCebPfzCKWWxkVozumJF9fq2XbOPiNyCF+UA2ND8Edv2cwp+Ppg/dGaifeJ2Xgfvx8gZJXFzrtfzXKPdizA9FZCRj0SAv4p/qBmoQRj/MrGR6YVSl8tyn3ob5xPhhu2pWFgOwAOCF2Cz+SQLUxV2AZmRyNQ8gzgXEx1zOJJaxig9JBZ4A/NFnBwIdds2GDtmltJyLga3cDLR3wxiuxmbqYE5/W3A0n9KRZ3iYNMzvJFi8rLzSvJPx8bTLnRmEuXD9HpuQRYt9u72IMW4qj9+pWWptNPjHRS/gHm5jMetgty9ofvqZ3PXzc7zM+VzOxWzhVo6lERnkcVUU/zM1iX+NcvdjOJCG0VInhJwpwVTePxPC7qAqvq6HE/2OXyfQIrtinPAPtqN+ZxIYbI0JRJc/G9vW/APmPW8hbfiDIbzm3kkPIAMfXny0BM5x07/q5uG/bl9lYvPp13kL2I8hNKE/HqBBg7kcd9wNvPIKnAKkMINWfEYOM0jmbE7lLOAV3udDclhDN4zmlUzgS+ETLLxc9TiAOhMLVJddLbn9W1E3L8LGwGGU8AavAa9QQium0DV3DZ4ll+HlDIZzPtN5h+acRy4gWuNlAPBO7VZ1ewyq1SXUgKHhO5YrJ8yg6uBoEpM0jXydyzXqQQ/1oIe6lTr/2U0WfSlNkCJoI+ju/na2u06JiajHbqgHrrRHPRoFfnevRWOQA5qEMVsiSUpQ2iGgT7ehjBQC97YVc/Q0DfSke6xPUHm9MGduPbhcPVhYet25GOsmuG5/YrsS/eUcD5qT1EvaLVeih/bBow+ZoGlIz5OtIR3GqKCghxynh77jR03jWl1GN2UwWPtSomlkqyQpXkd6rcx7MaqbX/4Cda2WdvcKegi2un+jp20W1Hbh0rQWx8m/SdpSdsdyqCQJBgh+EaDd3P54FyROk5BKnHxN67G7pnm92sw4PcETuoiLtPSfxpbZ0ZS7P5al/V6ur5HuibtHiYnh24RnaxSS4iQlFiubFSIZJSevUnLyTCUnH6/k5P5KTn5PyckblZx8rJKTE5ScTNXFsTolg/bshnxr0RaSTcZ4tOUalJ/slZKTVZScrFSSlYgjDyiOOCUGbXFOprq2UNeMJIfJARh33C+7uemGub/Xll6bLEjWPTygLWzRFrJKr2lJwEGXv5x4kpVMspygF27Pnr20ZbV/RKVpLyYongIN874u7dZBBdlSPUeC5gJTnudxoBawXvWSR6lgd6+UgRQXUOo+tw7V41rAI2ghY/VskU0korfhI4S+YJSMlBwneRyNdNMk7QB9vjPJp6Cby03TU3BS6e9VBMZ2vqvc8xJ9SsndouRGjfQNX6kwvlBKUEy5b690wLz6mQI05e67xadvvvkmLK2r3C+RNPE3ZachfGiTD/mqW3qE0ua2kCx7wH2CfcVINNQ3VPL5pEKffF6fJtNKA0A3cIO+5/vSem/FaHh1/RBEEo/b9sH3+lOlr1/ntsfVEc9/RoB7nkojPY1PRfjUhS5B6XopFckXpNwf4zL5TkCa00E+91xzV7n7pVkz5CtGvoQwZQrKrtY2GhDU7ylR060Lar/SuniRipB8F0pDdi09v3oH6Pd/l9gmpkjnhrnK3UeefDilFNm777pbGh1T7tstHlAm8YLVmkEXFTBOukUqGFOgtczQavwzwIZK5SNlJkmZaYXKcOaLTJSRuVCZmWvDZI0yMycpMzNdmZko8zSUeQ3K7IGaCBVWdHs9Ag9pFl2UyVLZZptMQZw8KSg5M0GZvXqpqU8qXisVtVivIzhYySTr8H0aaO1atHatzfo3uP9XWQ6wmXI0ubcK7TwzSh6lCukVpDsuUiGjNZc0wVpXurp5nOL+9nN/M/Q5mVpLcsR8C95CW266XGv5ROtopK341GLpcHk7NgiqV6hyz/46TVvPfVhKWKv5DTcoEykTyaG5eA9dfZ3lvd5xTxCQ50GZRA/gvG0yQJAtyNRiyi7zJGEbqEqwZbDMcMlEbTNnaWzD9dL1a6VP1qqEWVrDP9Oh3I4mD7h9cjXo/ShpenOJTo+i3E9zlXs+eWoEymSmRjNAN6XcrZMb1I1y36kMqj5asp7XgJNIZwEJXA6vX0HCpyNoRAY+B3NvNnwjVy4r4MB8IH8eWziXI9fDBk7HSHThyKeUAf01tlVrHXCAh8GMwYl4jYsFZ8O1C8A5jCYP3MkHNMf2dh3NZU8U89cvsOe7hdyZm4Uz+CA8Y8bgfJDB/f99mPUTnmDR3y9y/PER8j0K+K8Z/b6kEsGZH6dsROIgnI4ZEUtxBJBnPJI8jHftR3sgIej3GiwqUSkeghOPTuaiJ/elzaWj+J7jOQx4hiV8AHzFONpxPC+Xmn83sAdGM4R9WcnFnMIpgfzuu5fThp7EuY9lwBWfIGDUqf/Fd+8P3PdCDmPGAMwlj8MZxJeMBm64YRMz13ihsBF5hcvYFPcfxowBx1kPb0Cbca5fGoHfudAwjIa4PHComnAi5kLNB6znNMzbHphvm+swfvQJ7rE/6cwmNhLiTWc9bOQsrvAkkvHOafT86lwepwGN+J4xHMrpFJbmGUPV8QJGcU3GCBdlcQs9OJ3/sIE3ws5ciQVWpzXEvwTvHwJ8nEHPO+L59c8XyWEz5nqudrETKfceZHIhlzOQW/mR+xGp/MWBixcx2E3hCO76FkrOh6Ee6DbhV5b++TzPXvgL9wBwIDydCH0/5c+E33n//QjFBD/1P4rxfAvl7gXbbDtTnGY0btqXrpfB7RyAc3c81+wBS5cYOa9nQQEPfv8j39wky+7wHlC/HUvGwI+RKBqbrC7xwE0hJ4YA+1SivaIgCbgVuBN4FJau+JRCfmdMUJIxYZfkEMYi+ftU8uodzrJD0ki+5wBuZip33X47Cy4oYq9fIWXyasazOkqejXA4jMHcxa3cihDMmMo+9XtD+0MpZg/mchP7Tr8Dz9jr2TzgOZKTp/Lpp7mUMJYfuYlbyeezqRfQi33Yh+nAsxzr+5EDxoDjwCvfQ/Ei83i3GWO0g70ks7e95cpBa2yrlkWG/dk9eizGxumCeTC1NjQKp22S2eTWyEJDlzCRv3zAon1YCDQhgZvYn/24i5t4iOdYu83hCP/tmIexsAYTLdh5N+rTkWb8VuZMFhZwvesW8HzrBvneB2h4DofwIj35q2YqXQF2GuXejK4czoVcRyG3Ydxpwzz24zcS2AWA3UeC73loOASov44Va+byzI3m1hJ6wOpkOPRnxiXBlvJCn+cD4wSH/w4/JEGewwLMF4PNw36ilDP/JcAy1rb5gvt7e+l36KEc/PDBnD5hDDMXZLOApnxMN+7Tj/DAV8BguDWZXfeFRpsgwniCP02KCCgnQ0sCEUMrB/93RwkQlxLHIX0Oha5A8s+s52eWENlToqExttFH2GdNf6Avv/+ez++bvyD1sXh6H3wD3SZN4t09v2P3WZtpjXn0uy9Kjk1I4CV25wgcxiLyeZ95EzrwxYRDKWYrk7mPvvG34pndmV7Hp3N+XyhaD/xsrTEb2IOOnMsa+vITU3kOfPDl/XAQsABYhD3E2eXUo3rQG/vUe5/gsM8DsS8lB/M4+RAe4DAO5lomUT/oJbMaa/2vgvKcxxIm8hj9uQkHMq7lvE0L2FwymiKWb7eL6X8rTgKaYT3lR1PsI93/jdkQOITQCc432FfgERty+fHB7+zgd8D60+hMFvvzJ8tqtOZRUNfr7dW15n4cJyoPqYCCEBbFOaBpDNYyVmgJAdbFqKdHSXdKE/eYqPrL0LJlaNmy5lq2rEWQNLHjPrRMKD+42iuQWnukwt+k/oulFsv0XItlatFimVq0yFKLFs3VooWjFolN1MJJUUN/XZOSxNKl+qtriZYl7KsLaKAkRqgFC9XCTdOUJWqBdOMlT+j991vI47RQi/qN1YIWakGcWoBaEB4PNEPmxAgZ+6RFOdJMRstrIfAqBZRMPRHfWPVbpGlZC2mZIy2jt05PQ7RANAlv80bu9acLigWLBXFqysdqwQGldWyRlqYWy5apxbIS/dClhy4ANUxGNIrWl0MFC+UFLcaCLNjxG5SA1II1bt7z1OL7Dnp/GcpdgZaNtWAswbIZM8a2CJI/3XP/rbX13A+VyoNqFHZ8GmgEyIyrLQS7Cnz6C6kfEmwULJOXlWpBkVrQSF48QXn0VwYrtAzJ119SspTD6Xqo1u6r7iUJs4uEt200aU4o68obNjY+B10fds2hGKU3nVG6mc0Snyu7DC06TcdyqX5gpnDz8rBajZAe5U7lcoL+iBlUt11O5EQpgnI3GSyHFSHH3maUSpAmOsEGko0yReWXv0UioghRjL4rdlRc7KmkoGKfR8WHTlNx/bP1kscjj8eRx+OWtWad2Hc/OTyoofhU4sxXkccG3xIWSfh0DyWCYjVKzZdu/kOKL5I8XSQPkgdN8SBPqXwsj6eL+/8J8niKy5FV8nh2lceTJ4+no3vNxfLsNUEeNZDHVyxPg2J5PH3keQZ5SpBnJoG6g2CcHCQPPnlYLA92foEn1Djpw6If+chRD3roBdBLJyG+j9aXxwjml/4OUD+v134Uq5hVKnbz9MuzTRG7m0E9WB4PKt8vzWrmAYsijuADXcVIjQ867sEfctARDJcT0mQlcpxiefivPKB2tJE8RZLnHe1CI3lC2qSRoFi5+ORDepgz1KMa9mPsSBItBCKgIzH64rhK5rMe1D9ofHQMGhc+LCzecVGuH8Uo+XhCJXi00ZMaFjP3GYFPHv5WMqgIjxqzWuMoUTa/6BU+kxNT7tsu5Sn3gxisJWHK3csoXX6RNPHnYOUeJ4gPEncmHG/ijR+j+PiCSsgqxcej+NxsxRf5FF9YpOEFBSoomKKNG92y4tYJp0j3UawPDv1EHZbHybcVFXqRjziJ0bqH+8V+8WpUGCcVx0mF8VIBpeIrQAWl4g3631FBQXw5Euemiwu6xqOCIq8KhAoUr4LCeBUU/KaCC89TwZmowIdycoLbfJzuQCrgSxUQpwIPKthiddIxAW21AXth5RKnHthsyesgvNH60gm0O2g+6AgQeOQQr3jiygQ+9mLhDsMDXReD3qMmgiRXVmZrOMfpP3jkDTqe7dbX4X86gyJNCVHuTTRuXLwKrvKoAFTYBqkgXirwqrDVfBVwhO4KKSNeceRpJlIxRXqGp2v5HmtWHsa+vsaCJoSdc/zjqYI8EkD3YR4/fw4aHzODxsUK0GFE30cyilF6iie0T19jrRUQvP/hGZ3KmyogTjkkKp4CgU/jGCwf96iEEi2IsWW2FTdDOY55HMoaF0qAkreg56c9mZI6hd5Te/Mnxebj5Fb4dFSQw6ki/zVnUUJyJerjrtD1iAePA9yMjhMJI+4k7ti/mDu3B3vuuSfPbXqPQ5nJDz89S9GJxTg/eIkni4Hsz8rnxUmzS3jr6SJO72p+LM0yW15Av2rGJ61BHiiGegvas/ch5rzlNyCDa1l65bn8fNEWDlxVbIvIPeAAB5atCm2JIqAHxSx3fyPK8ZMggt1mHYg/4o4P4SMV8ymyG+abxo8soFuE3HKpi/CEHmydvB0f4MOHL+R2ewPLGc2RDKSEOI5hNkkMcc1u62g5zMcjW6/jRdrA8nug2w924Xu7QGOHk567gLkPDySb0+hHEcXshhK+wVvSgVN9XlrKPBP+0zEJc30xFngT+DXsfLnDCPPgeRf2/GdgnKXLMLvYEZgR2+9v6hjs6VLEnE7kCp6ghLlsng4dDwLmwq97wClb4G9uZRUe/qCYPngpIp7fcLifYmYieuHhiirf/fZjJ1HujTBTSGT8BZxf5ujjsCmPpE3DaZfWDjoYR6E+wH/huGOgVZlrzoFvusEXU9mw5gEuwgHehCe90PgRGP27jUI/Fp7BSOL4kj/4Yc3+XN0skYduak+Hm+DVpxYxIP460tlI3z/m8tSzUHIKDPO1ZzJxXPo6JA6CF64D3x1+z3e3EZmqWUO4Cs5eMJmDi2HtJfE8vKAD7wBPcS4F/Mzgb9cyZDms32ou11hgD2QkO/Qd2Cu4qnS9cBfCWzB20KsEOErfAS9SF94By8OuwJVsjhCBdyEArZlGA8w7zv28yXx2xXzn5K4w7tdQjuLh4iSY7/Kh7oVrUw7mpM6D6HBbM9be8QJGwlvAVcVF3HAR7FcwmFYv3I9RL/9ZiMda7Xb390qsnydjRvDwMIvR8DBGK9gV/6TIcBXmT2oD5isoOL+/iO4y8DGG0Yw4vuY9Xi14lfkzgZug7f8g8RZg4ToaAsm0YDiPlz4jP3McfTiQTW7+tY0KlbvjOC8DRwJrJHV3j2UA72KhIxcBJ0rKdhzHwRjVR2BcrrMkTamZqgcwkPKDt64GPi9zdCKwJzCcxAK45ElzDOXhJKAxdJ4Nh3xbmvpsIIVsaL4G2mwke53/rb8GTvFCZr4RRxoGl/ERpwKzxsIPM5vw9utv08Z3PDS5hHP/8wJJX38PK6BVJjQ4oT5Pvzacxng4+yxI/+FzCia2oHuXS9g1CV46B3M4vuAtKMrepnZqAexGazpzMPAyz2DM6wCa4mdbn8//+OjbIvocAD3jO/Pm18NYSTEn8iyX8g77kssuM6BwBrwEvFdB2WuI7sb2XMzZbnvMQVl5yMPK2wtTBhDug31HwpcYaz4cF3AKmfzJWGbyMt35hqMx18bvAFs5lgJ605gC6rOYM/2tOxpOJ53uB/dj2UFN+ODsY7jkpXN5Fvja9yaNNp5OcVEXOnIw53M9/yunZq2xh3cGO477MmFfWWvc3z9js/aq7Dm4EDiVyFO9jwnERF7qSmVwOJ3pRCfWMI9XedUq+R489yKseQQOWGh01o+oz8ccz9vAND7lJE5lT7rRCmPivFqF+6gOVGbm/io27l4POnY9MFbS/Y7jXO/+vg44HOjoyl5YTNW9qrPCkXAcxhfOx4Ijl0UOkULUrgGy6kG3TvDEZeaCP5chQBc47CtI/xb7qOtDZyCdCZA8wV5d7MkZALwFqdOBQjgQUg80FmEw2t4Fe778G1x2H68mnsqU8U/SNett0u4uMErj/g1Zf+V+vP3a4/wCzDgOLnGeZ+BfZ/HkhIvIHgjTnsCmND+Ohi3b9jj2Bo6hOUdwAj5eZjLhSxaZ4N7Vw1NeZouviLy94L2UPXn425vpTT5/cBndsU1NzTBPkbdWouyrsa+iTlg7B+NB7MFuBxxcQT7ZmHLfccOsxGMtXd62soc4inrkcitreJv2wEQ8XEZvejEVL0eznN1Zzs8M4o4gWi/A9/Dt7sxbvzf33BfHmpf25AUmU8K9vPlmAimNz+R/3RwezornJ4qYRfgLHJd0acz7pew4yr0Y+/q6rBJpdyd0Ix1Yiz9K6MLlFgJLL1XxmBqMvwi40q5XD7p2AqbC9ZPBt6Ubp7GWRNZyG5CID5jMvRRbLJkc2LgSjqf2lXtlDZ5tgRlBv2cDzd3/mwOz3f//B5wSKV1NGlQf4zEJaQ4lMj8llWUMnKd+fXKlpRvlo76ap6L69VF8fHCaRBmLZlN0md5GbEJsSlD/TfW1KZK8UF+b6vXVqtLr0kvzj+MU7YZPPgq0CZ8600px76KrNqFNweJDm4rQphK0SWhTMcrdhBRBSsKvrVDirZ459bXJV1+bUtGmFDQiPkkO56gLxVrDGjXAtsnngq4mUV6SK+1YayDGbtgURS6rGYNTLUtLmf+YTTKvouHn68sch21WEsN0AWg6HkET1WeTFpOuTXRVPm+okI9C2scCfH8uIX1PkRqRIx8+NaaxPC5N8rzh50nj/1YxHTSFyE7P4kBZhFNpq08S3HLru1Jdzu6coDyXEsrKCpetbpuNr5ayp2hU0iY9lzhSB/RD2oh89Y1a6eFHwQgB8tBRjVgjHygHVDR6nHS19DVS6o7KlqGsct8Y9L/j/419Xe8bdG4ssGftKfc5Vb62H/3cWvjky0mVz4cuuGBb6/JfBZxDhclZPjF9eoRrRmooPvnIURHPy0uJltBKQyLln4f4BrEIIZQ4GfUmzMmUK3OqXPdLrJ5pPuHzidRUMQlxwTNqiU8HMFOJQWUdAIK7dQSjtbRmBu0/VFrKaLRxEc6lu+NBgh560W3P6XRXMlvkw+/PZrxGIn3ERyHXLyFYuX+vRjSSEiVfkBO18zjPHc858oHSotQzuUr3VDX5L2ile2+FWIDyihgtlZE2RHCqFkVOr+Z7GjUK6Qnk6+eqLR/ypdrz50/TOahujUHjGFeq5XZYx2GUo9zd39lVVe5YIKBJrmzXDfqVex5z9CtVmykEK3cdkCrNQVu3ouzsMGl0lrKZrGze1LuHHCj+jpRfgsz3e6g8yevKjpeyU4uVTbbJgnRl742y7xyp3OzPpWkN5PMma+P6Bipp6YQp9zTBONEgXqQg6iPSEKnG102LIFV3UxtU97Q0LQVlp6IRCc/oZKQpzFRiIvJlo10b2cN6M4n6iJRKBZX+90gDwe0iZMORX9IFtv8tmx4qeBK9+fSp6sdEraSB0krH7niN5GZ9FOY9cgkzJAr1EFI9vpdDI6U5Ut4qKadTF50ESiBBAxkokSMRXbnXpGSAWrtlN6D6Zu5tqFipC9SX7aO9HoDFHgg+lpyMrklC8qKNrRsoTdnK2ZitnJ49lY3RW3NAee59O6B6jFMaUhpS/X8QFXK14zjNJa10HKc5ARvIcsxW40crIluUkPQ88DyA4zjaxnqEYA3GiqlKZn9j/iS+A/gDzjweZidfA4clw+13koD5/HC+/hKKpgJbObjhGia0IzyUE0bO6wJ+U9YgGJY/jEe5nTeLnnYXuBOBH+CEMTCnGJZ/BtM+pXn3V3m/5FgOPzwP34sw+27gF8umIbl8xWXsn1McQv8DW0/dFPTbi31KRTJejsAcE9yBBZQL4C5gMFlkcTZnwyZIGwuXXPQqu88+jES+4jauZ3whcBjkbLRQdysp4AZ2ZINmbaMBZhZ+nrIr3btj1gJxDIPwvDkPfr+RrqNHMIytHE1OUD9egK2Eb42QfzwnY4G5L2Ijm9Sf/dJ+5D3PO9zNDbTlK8aQ66aYgFkxcmvgXqNjIzYmq7rGnYhF3noy7HgONp6bR7jmNcywF4y/qDz9NQ4LcH4UfsPtCKbQk1O4PCRdXp4ZHccDJSscNvVvyIGA92Yv174Nx35szK4TCDyPIzHrC/zCGi6tdXrqtir3z7BYYfe7fz8NOn6J4zjvYIbUTZJWbnctK4kCqk452sJiJnEDcB9suZ8Z0+9nCpNgUzxsbIKXG7iyzFVZwAtmmXoQ8Jri3B0wTxTv2BvmQbi9eCPrWI+RucCaPDjHn6HhRhq0KsHzEJx0DegDYAX21hkCyZSwFzN4iIofGD89MFy1gHVIC8w3WP+QM78C62i7eDWPPmZHEj+EGdnd6c94DuAl6jOdXoKrfoNc7qIrr7GcecyuoD7/LnTAPkgviHCuPsbOEsfzB4/1yGP/ce05dsGuNOSvMCNi9Gi/dvZbxvE8pvJ+44RrRIPVPfmKRsRj/O4r8AL9uRUvT1CWUlqTiDT2wpEM3Bt2LA5T4H2xz/k+7rE8jA7xmivB+IPIrpeqUtd3CaZBtiSHrkHEjAe5kQf4inVMBVazC5RcDr9dwSTgqo+WMXUejKcPmxnGcop4hGtxgKN4mtYUk0UhL9aBS7fKUCFHYY7OGjmOswwjW98PvOc4ztnYuPF7fP0S45LMw6Ydw8tkuMNhNfbuvw+4mMG8zEa+Z8EsYFYLSmjJ4xGvwabI/QDv4cRRL4gW5H6sNIVEBtESMMboJPAWw38eLyVT7I4Z1WE+DILLf8de/X2A/2C8LheXVsv9wnl52AJaKb6CDnNJKNyFlk8BQ+HT54fSpzidTYwjh284E3sAHgOOojlZJLpO0mIIwP+lXBYNgd4UMo5PaUEx8d/C4IVwFIv4q4yfzVA4wLE0I9l9dW/mD+ADjsIovi2f+og4jiSbfhTxNyCeBoZiwzOcVbIjIAmibuwpwZSHsIDk67CNay/gEtUIhDLfXvgg5Pnuiu2a+Qmwlr+Mpoc9T/Lf62AppJPAQWTi52tdPAoepDdfcib7cCaneEZzxbHw+eeQWLAOyGEhSbxUDXWtKipU7pJOiXLqoAhpRXlbRWsRjbDPufBljEjwYRSsdm3hgRUtSWgwl7cbbAZWgE6Ehe2wjl7BBvJdD5LY6DsF4DoejrDlKRQfApNsVAe57buODM4NJseHT2dqYmfO2mI4MWw70Rn7MH3oUE5O/oG297dl4Vdv8EfxRj4hh3cx/vlCN+ktnMNl2E6BGIIxFWNahyONlrTgYnIZx4mcDnB1K2jUANpPIqHwMdous9e/H+txN9QBHhzu43Aakswq4ICGcGwGbCiBvovhFk6jX6ssLtl4Cf/LjecynqALC3mPdvSi6hvHagLNsKHvwZZMyiOKeoFB2BTqeijdBtYSm1WCrVhWh3IPr+VgMtgTv3I3XP1Qa0ruWEuDpbn0Zx73e84yK+RCkFqSwFUcx9Hcz0SWxp3Ggvth+Dj4oOA2mjCI8EXQWkN1+ojZVmE7jQqR2DJPYcFrg9PFuVI2jwaCQuWs90m9JD11gUoKUWERKsxLVGF8gXxxhVLc/rolDsVFEw+Kw1EccYojTsRVpv73CQprWZYoWn0ySVMhUjrSH/SWz4NKPBYkmqjtF5Py5b8CCdYHtd80Pf98sYqLi6Xp07Wl3Ou9cijSEqQhFOm66+5SYSEqXGFslEJQ4fQsFZ4mPctz6gMqJNVIAqRprzq8d687Zr4EbbRG0HzQdZQ1hIYzYYZhAas921F+sMQFiTfsuP3/tTwUy8uX7m9HkKvdPvMp/dCbdRFt9QuIBohCRKpXMFnPIJXwRukz4hc/W2Y0o2uyjXdux2GRlHu4hb4pAQ9wHSLm41GOJ8dq5FygNz3I0xV5fMhT7NHqYo9UjEqKMY+PkWQkKmYfFVOsIoqUkpMiuldUf0fGrKhtKVuXvqD1pMmDlI30Gr019Wn05mv2gCWCiqg5fvTOK6bc01mvIsyFLEyT45ytESNGVKjcHbw6mCKtQRrCUXIcJ+ANFFc8WfI40vk8p0mgBqTKV8fKvTEWR7eIUMXtw8/ZD5ViUH7Q7xL3WHW5Z15HQAeMco/FYy9H8xb6tW7mTn0d8nx4hDNHcJIcgl40HgR/CfL1DNKbvFHmJVTXyn0n8C3zCeZeKhQK+70W+5ICWEUk+Gx95gu4cRT8703wzQV2SQDm0gsnKN7SOGh+FsFuQ54HDj8fOHkS0BYBs+vloW+pwHTv76MIeAoYCevJoBd/hp0swT7kH8J2lfYFjoPbp3LkCHimvCLDcTGQCVnHwu5Hm4suL904jvncdjO84tju0W8wLkj1hqD7FyENvNMxQlUeSOKdd3ws+aIz3zCbJXRmdwgs+2F0s/FAd/xGP5/78JozrD/ddAf6rBvPABaxGz6+Bhx2wwxgNYHdMSLBse7vXtjYuAazD3mw5VE/g8sPJ+z3IcDMcsrZVM65qqBnULl5mD4Yj61N+48/gfhfiEnYB9qPp9jEeszg6D9sC0VDgVMRx+JjHJF0UV1hJ1DuTQmsTtpA+pBAZ70DjML6oqJoKCcBTzwAJy++kP4ks7z4US5aVgxczIqQ4bjarDxHO25pXm6lLCXL7Mxu0K6DgMuN7vUe5a85luIcYAA0IZdPy5gyhG2unoW5QsoEFkJ3aNE6lI9aBtlYTLEPsDedYPQP8MC8jmTzOJ8CG1nGAxQxOjvwEF9O5f1xxBCOLHI9V3FMayhw3gMeAcaxdSusavRfPO89QOsTbaz+lwBfZj1meHwbN+ari+4YUasVcDwfcAutmHYFjCo8lL2fSSAv7iqOGfYud7x1GzcVPcqcau655hjjal/M0tAYm260wiYC5Y2/LELdmk2kdui0fk72ORj1sR52H0OBDbwKfEIO3wM9aciVvMpZHA8Us4qXKWu/e4c4klnDL+RSRD2eobk50HNxE9Z39WrsjsrHTqDcQ+HBOs7THCiClC3QOs+YvhXNZr/iUTaNP48+B+/O7icNY836HBa9+hJcNTqgjcfD/InwYT6uN7JZgIdJEXMMmgGsAPI9xNOAkZxLJdV7JdA36P9uMNNmQWPLu2RzLnz2jGmHDcAc+HVRb+YvGs6lHMhMRtKXQtpj/kdauqWcUU01/ndhKEY220BR/jd8PhKuLDqCN7mLta7CXVvwPg/MbgqYX5VLgFcwml88DejGRRyFp/TLsT/2bt6PZEZyKZ9xNO14jXor+tO8qDvQE48uo+saGKJjeYjXqe7XciH2RfA+pqwbYkqzGaEjMhy/Yz5Wyjryq0HEA1da2yYBR42FfSfbHGckHj7nv4j/0J+3yWY2sxlMMYcwi8A3dajPqgTgCo7gaT7iUBLpRUI3yN4frvoaHlloqSZgEY33qJWbLIudTrkLs3R70qF7BxhcAoM3NWR9YSem//47vxB1EQS4jT8ZQosezWg+ojdNdDUPvP4SHAkTHSjqAW06wPI4PxlSmAutIORA8fQITJLJwGSHIupxPUdV2/1uG9YDz8BNxrhMpjMrOJ3WnMnNjONwrudSjLGwAvv2mFyX1a0E6hHwKDmBbXcSVf04nZYcSSY/Mz0PuN62jH2H3089rF69mutvuQWAc/r358KsLBbk5PAHkEI6R3AfvwD9+J3d2MCuwImkMZaBXM/97MPfPMO9PPfe7RxLd6YAcSVw5JcQTxu2JwZAc+zlvoxQ97nCPl7fxWbr2RilMD1KPn9gy0rvEOkLt2KkYMtQFX197wN4ersXgM3qZgNHmmvbJEAL4KfJsIwkbqI/cD/9+IN9yWYaMJuN5PJbOU6T44Ej+ZVD+Yqf2JM+JLaCiYOTeWnKPjyyMOB5fhEB76W1jro2pm6/QXWCHnNzCvct8/77aN06tG7TYG1YtUolGRlqGmQUScS2R+MgMvzXjdND5GnzCZImzpTPNcRkgup/Ul93rcvQunUZWreuodatR+t8aJ3QBqGtQpqANmSgjMpInG3VziBeGaQpHWRb1DOCpKEszmaiSE8XcRmCBCX56+6XdNSgwjKTlFG/fmk813TQvDS0OWGUHkUazETJQcoIhCR7HPRszRmDqkUSQfu6/bQO26ZeHb5Mqkc+1EVIX/CTHMf6YQu52pceSoiQft68eSo85BDdmpTkupBoLXOGt05LaCGBtpKkzzjS7SqfVnCgOpKsZ3hNuUiTmSxIVQY+LUTqw17bXP9zQVNBlwS17zpsq/1voEcJNYpuCkvnl122sx07E+pYrmEDlJHujuv6/ufItv9rAtq8zn32vw/UIfh5SSBBGfRRBj5lsF4zaarTQY1Ikjl3q0y90jWJP3UBuTqBdcomW/nkKRO/k7NxymCLUhlVk2NsZ2bLRFfupTJ4sJr5JF9JiXzxCeroHr8ENBHMT4sP89kCgmc0DJ98zFQeAebNOMbpxlJnYEvMYl5o1zbzoft8yBdNZBJy6/v6H4qhEgtUhFdeihTqdGy54EnhnC3yCsTePtVPPF1XgSYH32M2mlBeM/uQfLfIN2aMVrj3VADyzUYnnTRKl+CTmChfmtU3NRWNBB1Sc4Oy2uS8sLbYAHVK/wuVD62L+Unp6e5YSMmV6KERmAfFcN8848ePl+/qq/VR2PElGNPkHm4QSI3wyUeJEkl007ym81zlnuqyZdKQ2A7lDmg30I9hx54hwIAJlgNqqV2XTMdCTfqQ7/uy9ajIcdiJnCCf64nVhxMUiP0WwZhK1iNHk5Au4AIB6kxniTyJgOMwHzdqdM22xb9DuZcwRxuI4KjIM1hOeymlxKeU+FxNY1cdh/Fbk/xpUhCrEHugx0jQK6QohWSlgzZjLm4PIUl3kaJcUpRLsjaB4lLsWiflLcWn5CqljOymlNdQilDf8FvfipSLlOuVcpPly0W5uSlhkqzc3ATl5sYp15ei3K0pys2NU34uKs5FuX7xoeLymvlopJR4jU1MVDv3fnyg3ZP/1JveIuXzjNQnSRtXoJQUa5PfMMVZGw/r9khIP2JUw/K40Z+Abqq1+n2oNKTm/CTSUT0f2pKbK+22VfmcrmcZJvgt5JqkpPFKiSvQ8YzS39hXiN/l73G878bolBqxVj5SlOhe9zyvKT9o5p6CT8uR9twO5Z6Kubb1t+9Kd+wUYs6yPnfb2y/bwkmfSPnK+G5Q7pEo928rOxdUkoyUgkamoJSk0DqkELofox71lEuuK3sqF/QWXqWQ4orpjE9AucQrl3bK5gSNp/x9HSvJUXGwcu/oSOtSJFDuQlQ8cJxu58bS/qkh2ZmpkAEsw9bVFH7CNxEtOYi8XmP5vbgenfHwMLaG+IA/zVbMl8vd8OBbhRz1fiFvYx4ABrhJbuBJBvEbU/Z9kbuegm98MLmvPXVwI6/zAI+UqdV8M5s/fCR/cRs962QFbjAsWAdbh9Odi3iVdezDwcDvjMzrwl7cRCJv8ldWPqccDFu32g7Aeyj1XVbjeBm/k6WKIcxPjp+9UEyoo7Rwd1vhuNJN0x+4G+v2moKHMWzhW7bwLeTAlj1Mm+FJ5t7b41k/9GP+5HdbkO4P+wE5+cOBmykmjRTMfjDQzW8hSRSRQB8m8Rhn0out/IytY7+KkZ+uJkCRPI6KPNUYGmHjvC3muOs9zI+L3zbgb9+DCfgvKsH4WhW1d0U4wy3j6KPhzjvDTg6H5lPht/Fw5XFBx93tqWsilP8xI2hHKjCNWfzIqeSxD/vwM1DfJYZ6KWFr2JVXAvUZDlyOyCef96NGDwNri3eDfi9aIvoO2srvwJFDxjFycV8K+c7Pl6t17FTK3RyHeYGnscBQZ2Fhk2dC8f9g+nluBJfVfIM9TyMwxQKYW4DXYPks+G4viB8Cz7lhhv4LvMknjGM5aYvhzKcbIu7nmacvwuf4uIB4zmRPOtMIto6EK43rWwj21lk2m3yeZXotxkD1t4TDFozIOIwc4nmej5jO/3iGXvTnNtL4ALGCLXmw4G/zabkn5hFuI8YFjobu2H0Ki6Pqw9xm9cI8J4z0J2wNt66ClkWY1etgzD2li8OwF0n5XlYMImAwHY4p6aUY+eeJoHTvAuMiXL/Q/RuHsVJqEqKDGxp8vlV6OlxyEdy1HA4cB7TJpudZ2VAcD88/xZNXXMldW+czj2eZSho3uPlMB2zzfQ8O50sO4hneJouLMTbG+ZhrupaYonzave4MrF389xwNbTDlloYxrT7EeGCFhBqnZ5S9dJuQgkVN4knstpZD51nQ82kr8xJ/Qtft4KIcmJ4TnsvhHM5uDGUdwXGOxjGZfBKA1RQDx+PjIKZzbdC9RPLqsRDoTlMaA5N5hJHY3oFoxvkZ2DvmNKzOLxfAlL/iOI+nOSGrP++QHHH81RZ2KuVu8ADncjx3kEwCkMRK4vmZPE7kBTzYtie/Qu8ZfvnH9qdkHyhKgCQPcBqc9B4UFnzBRMCzFHq84PC6k8gLe8Mx3v/gozfebgeStGcjErYs5qQp7/IHLsv9171hvsNGXi5LAesN7Ab1cs1taDBmYBziRDycSGtgXz7lc3LIATpCQifi2n7BKeGBDBcBP5lyPxd7UPNIAOKYh48P8XAG53AubxHHi4xnNQsxXzxnYH4N33XrnoIxJtoSypbwo6eb3ocxhEqAs/eHPXeBOcDKQjez0+CctdD6ByzW3jlYglF2+lsO5R2W8lWUeWYc5tFyf/e3/+vsgAFwRjEs+s0YTMEk0xRM2W3G+jwcy4G3Ipa2rUjA3PKuKz0i2pdJVfCq1X//8TBP8LoH4uLiOOWccznj+qks2foRc/idpRi7xI9jOZV6tOYo3qI7X/AQxoN5GzgGcxIGMIUMXjCnR2RjM/CKlHu6e71/D8aHVM4vU0VwMN93HrDOywUmG7vpXGBUIhS7HbZ4Drw+x8p9ISSX3bBw11txnXNzNNCAOI4mgYHEh0wKPmCa/51AQ4yOmISN588J7h2r16n4x8wu9GQVDXiVdN7iXGziEk25n4ztLmmLPXIvAz68vMi5PInDl4xhVp2ExnZR1+vt1bnmvog56kKcuuw6Q6u7NJbqmcFyDKgRFlosC2NTtMBcElgeHkEXdaKLkkgSoKNAX4BIQPyNfs5AWbug8zNRPVAXVwCNYYyyyNI1Z2WJrIVKz9ooX1YXzSxxlCekm+6Sutyj2V1Ql86Ba7uAMq5uIrK6qMW3XZRFF2XRQVnEKQt0hZt/OvHK4jBldZ6mwV3aq0sX1KXLierS/0P1uRX5spCykLLaaGVWurIeDtxrIJxaazXiCXUhVwPJko8Zmkm8skCHY8yZo4KuaYhF0GkLGoAZVxVBNoPmxCF1cSUV6Sl/fZB+R74uaOZUlJeFlh+Lsv6D5mclqmRKZ2WB0uioNnyoLgwPaZtg6QO63C3TB+rpHn/mPpR1e0Nl0VZZOG4bdlEWScpqipa1QIswtkX42EkhmiuKbZU0we5hx54R3BBybDKTlUWWsjhW9+BfZ0/W33/7VJwmqc2+Ugb6llR1YVcbZ13Qki5jpS5ZUpcrpF2M0eRvn3kg8ZqE9HdQd2xC2qucNfdmbt/vTVmj6bbILmH9thvmfkCgpY+jrGtCx2awrSSNNHWhS6l4St0AjBCMFrxYmnY05u8oC/RaWB3at8d9RlCXLonq0sny20QXnUSS0jEjdmdQe9CM0vqcoyxO1c+01vPu8eCgH+nu8+D//Qc52oq0otkFeraF5WVhOf3Rtg6oxrFV9TX3Olfs26/cf9RjFLk5zZHiXKWSm6CCwR7luQPBnz7RlbdAD+OVQ7wSaSDI01p86kdvJRLFkPJ1vLjOq8M8KC/BDDv1gvK0a/ZWQ/KUR55S1qfojzyUlxdnko/ytiBfIpIr1yfeq8TEvCCZp8TEZkpMJCAJbhnZaGYeUh4qyvMqLy9BeWuN/mXyoy7mYgFycJRIoisokc91B3kqZrTyQFvcgZvg1v1SzA9IcBtNxKhwgDoRKMeHMW3ysJio7VqivDxXDkd5aUF1amjHU1LQH6Az3fy60U0b2ODWb4XGxe+jIm/kF4hAxXhVQKLySLB8E1GJ4zfEnS2YIEgNerD2Eg+h4970KI8E5cRbaLlEAka/AzHFX5MPn4fWcmga4VyCqwj84fiShWsAzftuX+VdgwoSj5ISZqvIm6iUXDQvDxXnoaI8lDcmuN9Nil3lLowclef+LU+5Pw+6eBvuK84/TpzAeEkE/YAp8/C65YGOKZOPf3w68oBO53Tluc9OHnlqRrqcoLLCqaPxoEQvoc9KIpo5zb35PKT8XlKOtUk+Uh4DdTFenYjFWR1LMAHja3kpUnyILxhHCe6YuTDoGTExtswlT1ygs15FsxJwmUv/EOWOfW2sITTM3oPYktx0bCGjYdC5G7DNa7OBQ2teuaPHeDyg3MG42s4yHcrBIcwZxx1kJaCTQHCJ+jJBG9xOdMjRH/RWCej1iGWNFdwojkDOEmMxFLj5lYAeDBoQDo7x5x3kOC/KcV6S0wOl+JCvBMkVXwkqKXHChFBZ7ZbhIJ9j9/ecc54cZ5Icx+7LL/66dqCDSihxpZlKQD4cjQlLO8ut+xPu7wZB99Mn7P79160GDXL/PwKj6DkOZepSKk7kfkshxa1fpgb+iEZeHV25f8D1GkiJHFZbnltQVpdwlkWYcgfBCXJazVbCBFTk2H0dHHZPNfnwHYkFBi977k9BieAl97cpd+vefeWMRENLkOa3lY9ClThetXbQ5w66N0o7vxak3HPcxyC7AuV+PuiwbbivZ0AlDVBJ+8B4sTFmTKRI9QvNI11Qos2UqIQeGl7aH06prMX2Lzzg5j0/LI/PQCXXU+Z58XX16wCk3gHl3hDJwSe4VCcSmKQEK/dbuEPfhJRzoGaR7OqLcDHl7uMC+YbZF/TWf5hy3w9bGQ5W7ocAce7/DwAPuP93w4KmJGIuJuYD3ppX7o9JSAuYo6bgynJ9xCFaCXq3dOBYZwp0Es8JNimOiaVeDufRRIVj4qQz7K3+M+Ez+HRBPVuqaZQoOEBNcNT0N9R0JbrmNqOK+SUQhLiBSRyiKWoaJE8/7TbDYqSmJr2aTlbTpisD0vjvoPsyqU+ybJOTlTEbtJIvtJJztBK0Fq9EU4mm2h1P6XUNQY1prJWs0Eri1bc0v0Ab+dNGo4E1JvC5moAtefnP/UZoG5Qnq0A+mqozjuLTUWrKLWrKy275XjVlmZqyUk1ZqTRyFI+UwRqtBCU2QZne8UrieJ3u5jfPVe6z6ajexAvQ0ZyolZ45WpphSxCrMB78NTX/0Alsxhk5nmemoKk7NvzHmroSL1LR4U3R2l4eaVkTyYNagRryulJZKcKCZ8N4NWCrrnaHUwnSSvfvOvbSOVHq9ykV00J7Rei7LaD3HdTUS5mxWS9CHsHj4lOQeUNtqiY0VVPiQpZnwsdZ6i3orJWoeCVauRKtnIJWNnU9SNazZ2Zd8HMVXKe4XmrfxL5gVs+WVvaWVnKZXjsa9f+1rQpYYhMx0Ci+1mbuUD5oWmk9EpTp9mPZtmmiDJqqKclqmnScmmauVBNWCXyaQTft447BGpZtp0JKGu84TtuwY8H2i4nA8e7/xwDvSCoAFjqOMw+z00yoqJzqQDG2sX404OEUejODTMxnl7/CccBwXuYHDgfGsSsf8RAf8yz/4ULWoFso9TC0lXBjSrb9KQTWFQF/sQbBVUDyrby3eBVTd3vepQHAB0dBfAFADt8ADxcDq0vjOAHw2GPn8vHHJ0D+Klht3luyuJwCkoJSBdxKvgjsApidPq/0eDvgv9zOzFKflyX4S/KzHk7jNM7kILbwOadzBlDM34RSyURo/SLhaYwmOQ1jatwddK4HFkINzCB8pfv/51B6R99AqTHwc1bzHHBpNszgHXJL9437ML7O20ATDsGi5DwDnA4UrYEHaFTKSDgdSCCPMRxCOxbyLA9zH12ZwCxO9w1HG4wmeCJm9vQHPaxe9MNYSaeVHgmmwaXGp/LRsR/BmUdx/sXr+c9COOwQjK94GfDZajj+A9h8Oy/kzmBcLhy3qTEJZ74BPljLaRSQjsUnejis7OvI4Q5yOBgwQ2Ez98wdWJtHwu0EvC6mE0rt86MB5ppvaNjxlYLVYdbGBzE/LPPdctdhnLWrCIyLjYB/pK0JuvYQjMZZig/cCr4DjScYQaAxMKQAStxHkS0mRUQZt8WQ6hbSpAlu0LX+JE74i2nXLOXouJZ8PfobnFNPptOGGxh12lG8130MxTdsoCkns5rCUC+oDbCp7g8Aa9jgP56fRKv8ZjxHIUdyKBcxl5mVjuRaM6gOtswIAmOiJaFuVZa5x2oNDn4uboDAl4nR5R4GfuFmPuNoBpHJVpYyndlM4ABWYdTIwb9ZRNFJ9MCcmRZjqiwcPmA9yUD+LyBmsZhsFrcGfkkCrqOf717iLyiCppD6J9z2aWgOrwBz5y7CO/cXTgpy9DqAn6Pe39HAz5hiDcYvwEf8ERaN3APcjD1q37GUpfzCFHJZwnfb4S3GHwkQ7HUXzIUP/n8Jfm6D+VPxM/z/dI/HEWBDHsdwjmMXYBYbmeaGPtsHewXMYA1TSWA+8cAAbmMAd7OUp1nDNJrh34tQwi98xy/AOWf347wZ/en5WybwByX8wliM1jmMYQxgGQv4gTe2uRUioRH2oRsZ8YnxDD5nMPf+egtbtjzBPNawpQUM3RvrqoOBmw6AvIboW/D8Cu/mJ8FYU9hXcj0N6IRpvclGB7oGe7uWTABW0sctqwALHAnwEVGi1ENpZM/22AvS+u9m7DVaqrr4mEBfBiOTINoi9p7KxmKg/ooxlUTouGhNkOvcIOzhNkEpfoeXc2DJEmCuMZ582ERNUe6nLFZSyO02zu6/nnMuSaLVoH4way55vzzOt/H3sNfBN+MkJjKWqfy09EiyPAfTO34l5/cD50BsNuNvimLIXxe0P8ZFD4w1dhA+4Nty6cO1he1S7o7j3IRpvyozyhzHOQ9rj2qFgG+xZ6U3NvjWYwPzDhwO4hZ6k8AlTGcjs3iaTfzOD2QAAwbBHpNgwWYwR7e3Yyq/rHJPxDaW/I7fz/Z7dmIpcEccMIB78dC/V1+WtGtA99yVXE2WVexAYAMsng3LtnzLAL7ldmzgjqP8gfsn9tiFP2gHUNZtqoOHg0oV5EQWMJ47gobdQZT1TVmMOykJg3/WtAqbdfsxlxC6elSERw/0l+W/9mG6cyzH0rbxclZ1Xs6Mn39lLPuwL7+TxCbWMJqf+ZZ4BjKAi+FADy9NfJq1W41TP4D6FLInj/E9PwAZu0PXRjCgqAssO5eSHm9w8Fj4EehGN07EwwKoZuVePoqA7+Twy703sXvBu0xiDfkroN4EcHVC6U4upwj229KY9dP2xHr7IK7nKpbvMpV1zeeRXgx7zsP1lAW/ltgktiVGaf0Jv0LNYnMlthk1xL6MTsYBbsHma36N1oA4ejM4wsjIJLDJD8zYthAbF/dgzr4GETrOumNPVjiWu01QipH2Ebx93PpVFHKHjbMHriCjRRJDm+9Ks0P3Z/CqMSj3Tu78th8U7AXMJHN8Gr1YzyFJ42nWCprvA87LQdlthS1uhfZlEIlMYnaXzTTxQue/tzDWbaP9gL8o/davG1TS4NmWoDV399hZ2HJLSpgx9Yag398Ae9fWmnu4b5lRoBWgj7H11uY4KqBAYo2yOUabUpAyvRL1JdCquahHj0w1IFnNOE3NkJqRr2Y0k8ddl/NLSyyiSys372aU9RGSAfqRz3QyiwWP2vEk1GwFct5BH3ey+vllKRVv324UVF6wbMSMm82g1DFYHGi5m3c+6OGg9M1Bi4PK3oTZIjZhoc3CDWD1QcfW8PrhLdyiDUM3SEuWqKhpU7UALWuM1Ox1fd7sBh2dsbeaMcPuYSGasze6JMl/T73Uns1aQTM1wBGMFnfmKuk9qdmRc9R6sRmkm5eW5dUPxFdrVKlEjlBjlpaTJl1gHifW0kMnlznfTLBBsK+4BQ19eajEQgmvxDKJYg05f4j4Fu39odkPtmLr2B1Aabyma5CmI+2KrTNv5nTtSXol78EjaC4oEHQsPR7PHurIbHtGCB2zP0XIJ9UdY22wUHnFhBrHC9w6rwiTRyO2aXWGdpwlKNBjd0r6UtIxC1TYtKmag5qxXM14TEfzlh7knSByRHSZxFwtpYfOuB/x5HHy8lPp8/U3aJ9qHFvlyPZRIQlT7tiGwiygcVi63Qg1qC6gFg2qc5gTkf0wGL8135GPAomOOhl06SVIE9pKPCYfxhRx+EPPcL5KOM0ltPhUwlY1plFIni0JZQmEsmVMfqEsjzrZTZtRifuKxDb4O0K54fIOZRkKXxP6gJUQaui7hEBYs2xwPRLWvgw95hippEQqKpC8XmmRaxX0IX1PKRuqBOT7G2mYP2zbHtpEiUuSaOjm96hOpERyZpfedwu3XW6lq8RArajGuh/BEVpaoXIvUS4l2p0eYX1tbBm7vX3lgP7DUPlYqBLH37eLNIQhpdc0AL3i9qMDet1ly/goJWPpARqqVaXvoZmgRA4lcuhYOob6BY2tBhHGZbjcTSCUXiT5iWhG5rJyHKh3tY6xX/UoPpWcYMNMBQWS45VYrhIu0u00j3CN40r48xl6rEvQfddiKMrtYsuMwjYBF2Fr6GdjX19LsVWCP4HngtLfhC3yzgYOrx0q5GPuoJ6jHMrOOh2M1ZFUOnPvqEJQkQcpHndmZLPbfOJ1IR4lcJoSkBLwKQGfoHGZchPCJNzNrA3gRJ2LN8QBVzBf917QexHu6aPS+hgzYbZ7XXyEcsPlZAIuWR3sgRxL+crdE5ZHLQ3MMjLU45E6JUhFCZIXuZ0g3ewq90bIl4caJKKZ8Uge9AIoAUcZZCgfn/KZr7701EN4VXhcgjQjvvS+/W1ahCPh1LJyjxccrlySVYBTyvnfDbQhSLmPZV/lgwoZqoVtFiohFyV4zU2tf2PPQNAyArPaPwnw3KcGNds6PlQ/OlT6HhwSlEOC8nFK2+rnoHGxNqgNo0kRFhA72visrGL3j8vqpavGy8uTSvBI3bpJKvBJTr70yiIN2WOTvNwalr6B4G9BvuCRkHNrWas92EOATiGw98PPrHmZWnG8tzNvYkJNeExXHCbp6wIVMF2pOBEHhBOk3EWQsktFmok0c5LU7VxdwC6C09QOaSb5mkkXzcSrmaCZoOeCBt5f2M6+fSOUN44pmsEsnc1xagjq6F7vlz6gO69Gq55EM2muGfxW+vC2xHbQ+WVXjJ7lv3YktkSTCHoT203nP7cMm0Hkub+bglpjvtn99+zDOO4zsR2qVWnvX8LuI5q8tQ19mQrqHIe6dQp8zp8P6pyBOrdGnb2oc2fjzrdz26YpCLrKYbY6I3WmUEn002OgL+ujzu12UWdmBsmA0nbtH1TfNts5DitW7ghS1NEt+0m33K/pqE7Mkin3fhr/ZrJGjkCdGar2cQtFJ69ghmxmbfkkE/pV2A70iavc8/zDGak3m5XEnlW6j05B4+4ubOnH30bhSyzhcoZ7XeW/Fowa6c//s7BzbwadGwduzIPy5YsKx2YTzeQKzW8vaXOhNLOrtLG9lizspJkzG2nmTILEo5kz22vmzM6aObOJZn6FZh5h54pndtTCbomaiS1/Cnvm/PVoTmCJtAZl5/YKuYY3WTHTAx9dStwrnXlxBFyg0EDDBrGRs8hkFV5s/egrYEYBcDe88EInkh+8kLNeW8OAwl9pcNwwuuDDyIRw772QNdOiE4HZwO7DDKqRHBE9yUiS8dKDPzkBM7kG0waXAPFjT6Z+gyFsJoW76IFc09NyLFzgiUHpd8PiPi3H1ryexT6nDsI+lwLEyH6YV4xiYAQ5iDyMRvk7Zjx7CujME0AaN/AMjfiNN2hPwMR5Ln5TcTg6AE2wEGufRkxhWFvOuWjIBWYXgzPH/Nw4mIG39wY40W/fmx16zUpgAqv4kpHczAsYf2M+M4EPNsPszdl4uZuX8Xs0DBAhk7B41WAUyZrHVq7DWvktjFq2iUQW0ZnXAYd5dH49j3rLTmY5e/J48T145rzGK3TlSuJKTZx5hAa+Pp/76cG+QMDo/QJwLc9zE6sjjs9g7AYRIw91xsa7f9y+gBlqfySyv5rvqJhK2ww4nIDTtuexMQnGwkrAxqqDtU8WZrTdH3NqV5GXxQGYA7ToWAN8DGvWwvmCN2aCB1pHvMhHCHG2BTaI7gaYS1uXefwLcC0hgTVL/dvUFXYK5Q6TmL+4PaM+vpRTenk46dzzmPzmG4zaujUkLJeAlxhFQ6x//sas87OKgLegb9+XOTUpib32ymavDgvh6NDhm7QG6s2Gjpj4kQqBwHmrgM/8P97hK4zfW4QN3HBa0R9TC3mZxmxiH1J5lXPd4fG1e82WoLQvYur2d+zF0Mo9/rGbb0C5t3VrFFraX640AHrgAKfRnYbsRgF98fIGxcAwQAznwlLq4lyMWe3F1uTexwbOp9jLsSYgjOEOZuAZQVmetR+rukLXgdm04k1Ooy8ObwB5vPsrrJsB3RpsxnvyWwwj4K6WybBsMnyBKQwI0Du3FUuANyuRbitGss0DZtOJHzmBZIoZxks45PPpmKNpy3D2Zwsv8STDeIFh2At8Q5Q88zmaYtdJmYdAlLmtjOJo1oaMo0joivW8H+9g7TEFG4P+IM+vYFTcX9z73Rb4CHVK9oX7txPG+hpBYO9FAvYCjnfrcMI2llkWiyF3sZGCBlKlkMYFefBq2IP8G9XthG774bjLInVbCcephkocTGse4/P4buyeD86hB3Phb78xYfNmNlP5TStvA11vJOooatYMmjXEPguaRUgwFfND6+JSAg9ICGYQtENqEC3ir+bLbkPsZxaMKDKlvTHsso+Alwg8EJFxMDZ7PaacNA7wG6cQz1XAXEbxAF/gV0+/MIBlbCUP4ys/jz1gz2NfCtnlll+9eBzz098Y20xTBsdh1OxwjMQsRq3B95nfba6L/8HE5+DCaq3pEZYxrcOON8bmkjbX7oF5XUykNa8wnPu5nh5M43/sjQOMYDynMJAOfMyVXMkiFgHQmtYsixJFtAtZPEhX9sNmzv7xNpCGfMsm+ru/izClHE6bDccxBJT3bgReWkOwF0xTArPtYMzHvr7KQwL26KRHKPMObAKzN/aCr4qf/+qGsDETrpw2U95uhlrHZEl7RjxT1+vt1bHmHpD2io/PVUmOT8qVNORoKSVFYxMTg1wBbJ/ceivKnYRybwyKglSelKDCSLfdCiklSLoGneuEfkpBZ6SYw62oEiECzbbI26DCuLD6pKABHjs/IsJ6Ym1JslvHE7E1V0WQQgIReqJJxAhd1SrxgmNFxDX3/wp+Kv29BpRLkk7nNcVRqD78pjWlaVOUxM96FOkjPtIutFEuxoDpRKuosWGzyJKQRlOovdgqSrsxTb8HtdUyzGHatt7nsaAe2Fp4SYS+ODConZ0o460HZo+J1Jd1LQVBYyaHqhl/60h2boNqsMQTrxJKLOevfZLPVxo3tE4afxp6KdJt+yJI0LmosViDZXLkOJZVlcdBr1xStj6+3oE0om6U++KwukZ6IJ+rwfIrL3fLDKIVK3eTaQKfzuJlTQ85vk7j8cnnKndoozikIiQfrTQkSvl+5f4czwl6KbW0G9NC2m3Odt6n/+VyG8ZND++L8wnsJWhZzpiL1pd1LXfU+Tiqsvx7lDugdNKVwUqNrS/pPkmFhSrJWqd1rNU64l2/y7UkDVC9DDdKeyVlWQZSJMkLa7oipHXbKe1NaecnRigvyAXvHCrHVKhuaUggsn00San7B0zm1fEkVUW5P470PC+rQdDxeaxTIdKt3KpUUgVt5HW7uget9D7o5gjl+5V7Hs/pe7yiXoYy1mVoYwOntA9/w3jjkeq/P2hdObIgLH2y2zetsY1KKzAmjf8raZ37VzugXF/OWKquL/xalJ2bLROObLKBo7l0cwJXPnMW56w+B88DmWT+JOAH3jvlJPLOWWaLxwBb6sFhXwOHcDZ5zKnOyuSU+jaqNIZiu8DK4ECCLIJVQwbmzuok97ewdfMLVrjsnwLKpSHkUTdbqTfWQZnbhjwqXsn241teZVcO4z7gZRIxF2lg69jxwFlsJYlcbqQI44mMIxu4lcgxS0/DXDwUAG9Rwk95G+AYOG5LgPOUg21OiYQ/iWyhycCsBXPDjudhVptUzLCfgQ3NegSMr3WJda7MItRdBpj9LZphemfCTqncDX+QBby81Mfsj2dTP64+tzq3wr370Oe2ZFiM9f7Q1lBwNYzcF265j5sKHuRZlod4P6ttTIp2Yjt8a6Zghq7gvLdiTpi2lyUSQ0X4kQCBFmBvelGPpszjD+bxPabQ7yHAcvkbY2BAAXh+gXvFTR7Y/Aowk1K/OLdh9NiDMAaLQ396cTmf+h6HX8zT4tNEZrZkAn3cms0gcjD0xm66pQTFw3WRgBlFO7n/V4Fwst1YjjkSW4Q5PRuOsbhmYGycXFdWU3tB3nc07MTK3TCBCUxYPIGGDzWkp9OTo/Y6Ck9jmPQaLJ8KOM2Ji7uMIVcDky/njLzX2cJympTm4IH8I+F7+KXoFzZ3WU+LNOj0W+WCOe8o2IrRGb+v64rs4OiD0fSqHvmyC5RyUsIxiQQm0Qi/iv8CSGQqi3kDC+2cgJdODMHrsu1/JHgPgaDT51xwWJ5Nzd+GGTNNeV0N/M6RdCeV6YCXNNJpw0PYw/0ahDiPTsC46+2w2LiDML76T9gYSQhL73f3m4s5oKxL5GE0zK3Yy+15bGa+G/bF8xw2/1Ed1W+HQ12vt9fEmns0cXA0l7laTFsdVXq8p9LqL5EWy5XeFsam1LiZKK3yST2lkxJPUrurG2jYK0maSIs6XWtLxdY7/VKzTJB/lrSici4UEgmOo2vyJOYbpeosibtlDremRzyfgcWo9ffV1xgDKDA2k9Uan7LdYXc1V5ee82Jr3osnoK2Lka41O8lSPPKxixaTp12RHkF6gufUGotl2t39mxBWj9ux8IKbCew43Yz5LVpJzRs78zFD+TL3b2VlIlXb+fovkX+XQbWq0oAGKqLIlV4qegoVFaHiIqSiRKnIJ/kkHSjd7NwsL0fK4xrOjD3gyINHXrzy4BV+8XiF1yucsk6Htkm8yONFXi+6wIsZPF3JcI+XEaKLP1+P+7sij5R1LeEvMH+9wyXbY24Fws/7/ZT4j++HKYzwcppjAbgD/RsoP3pb3S0LnXdC1PqnY35XIhmAk0lWkcuSEdK1XB2xnE/cPMaBmtJURVg4Xg+SlxKdzzMqctP4pV+UNnweU+aqZfnJrUdzQmmT4f1V1+PtHyIx5V6RxBHnCorzoLg4tFscUlyiFOeTVksqkoqvL1YhxVrIUnkwvxttuVPvMUeFFOoHCoVfNhaKwkJx7VmiSTXUMwdNL0SFhai4EClIitzjIbIVFbZDhV7jggdLAYHZ6ZvuscmERnf3i/8robb7xAkq/xAojbPpl+mUva9CkG8t+rqfzUSDjy/CAnRPcn8XQbkU2QZuOr+HzHsxFkgekQJr3y34QpG8B4aOs2j3mqw2QTP3Yq5WIWhuWDqPm8cg9tMKihTn6szpSIWcpWfx2BgOEv+13QgNhu0BfUPdKPfw+z/FrUtwf9UFO+sfKFGV+060Q7X6EY8Za2bSFmeXSfBOpi1YboTiVcUsHbiItj90ZdeT6rPllnrUW3oh+fcdwsq0s2DKo8wYciDJ3U+DhO/As94WMP3Wp6sJ0B4OxuIjuNve9iXgl2KvveDtp4E9gTYwwuM3jp1CqKcabNtjL4AvgUut65fBI8Wwn4zRAMBaUH8zRglo8jikHmnLuWtwg/rtTinFxzMUPA9BcR4BZzbZZdtrBbZz0RulPY+gjFuYENRzry1wb3catv57GcbE+BbbMeg3CrcC5mA7yEO2fu8Cb66GvgW2u/Y2rM1au3nWI8BGKiY6g8TBHDkswtopHWjAgTjcTAnNWUrXoNR3Y7GEjiznDqMjgWTeZAvH4Lir7tcwhoc4GyLuSU1if5rwA0uAmcAFHMwSJnIkuRyCsaLC7QYJmCuMYAN6MwJG3PKQg93ddIwh8zzmomARgfgz/bAd3osqyCufsn5X6mFr+8lBxxYT6qslhoiIukM1ptwrgAejJjocye373Uf3y7rbdvetwFclcFgSX/34OFvGNIGfZkHJd3Dnz3BUP4amjuXaZ/Zlybg/4SefPV1pbqaTMM0C5oxod8yhDKaa87CgbUMawRP9YcRoOzcGP+GuHWU2Zg8CMoZj5q+TSw/3xZRuKb0yn1D/Bf0ou2P+eCAOzn0BDp3pZlKCPW3fYFbHUzDtd58pvzxMGYewNethFsPTmvFt4To2ld50WcRjCrXEvfcNmLLv6ha73L33o4KuycaUSbhjrEEYy2MVRvPbgimlLURXGJdRGWdPJ2IOIJKB17GYqZupinJ/E2O4BDNYkkhmCVvIxOFWbmbW6Um0apbDvg8+iA9ran+9/8N/2IPLeYj9eQUxlFPpzhcMHr6ZVkUw701zE3AM1cdgKcIMvMdg/XQ15mghD2PUfIj12+UEQvfFUCuIqtx3erbM9sKH+XOB0TQdvwtnpnak39K2kDoUznGAyzj8iJNgVSbEfQ8p02FBHHp6L54u8rLl7yKazPaRsNDyK8HcnWzAFCJgU16XKdcJ84aYhOn++HXwxGh7eEKxkDJ++b731zjUA8sfFd3k764E4aI9IT4Jfs+FmUuJPL3dBG3XtWUoB+HwUuQZoM+9VudzMM9BnOszMB5zsvJ7pIsio2OEY+mu9Ao7XoIxXefj9+kZCQ72tj0TgCJysVdgHEai+wpTk+8TeBPHY6+JYqzTTOW2xGbA4XzwSFgOZUInl2BK3wFG8SMtNqZzeJf+HD98OMWvvBKipDfThTXsTyLmgOwpWrGBOPpuhA5F9kIcWol6VAU+7AviW+ylNAGbQnjce/kNC2e5PloGMdQ6Kpy5O47zMjYdWSOpe9i5/2J7BBpLWuc4joP5eToCm9ueJWlKhZXYgWfu4TgfOIt9oPGT8FnYzLk5sHEaceddTZ8NYxg0fxJ36hR6tZ9P/caWpMAHI/6EhUVlZ5AOMBibFaVh8VRfwD5//dgdmzOuIVS198FUkr8hi9zzG7fxPsdiymok5sAsGo5gHz7iOqaW66QM+vI9Xs6BJHeOnYxpoFeqVi8f9mKcj/9eU7C5YyNs8cDUaxH2ThmD0QEjw4N9Nr0PQE/WkER9TMHPxNxYfYO5F/uDhWxlDacRz2u0YgMLgwizB3A3TdmDdysxc0+lL1uZgS/Ij6eV+QN92JM4BnEJPzNsv/3guqspvuNy9vt9IZOBQjoBV1CfC+lHEXczmf2BLpzMfSzmiApLD6DYbYF12MPaCmvXjQRNPFzkYquHN2AfffOIvJkqhlrHtjsOw1aCe1M2hmprbOQvBhq5x47ApjsORvr97Z9iUK26tBBku1KsFKS0i6W0bKntqhLpvg1SowZSGtIbQbe7FakjUjp2zi/1yxqeHgalhcksbIv302HHF2MGwlzQeiwowWG10A4HY4FCKkq3HAvft72yCvQ/LFCJ3Xt3pXGf0pinNF4PaZPy6JCJJCqNNDWgQemxSWXKa6DstHz50nxS2n66MiFNaYxQB9bqET4Jye8A7tLJfF6pNutHrprSI2L9FrBA2fQP1GGXRspe+7KyPelqiSP4XIlIQyjSGuaWXpdF2fETLJH8uWzAYuY+DbrIHTdHUD6tNgcbl7vXwtiKSaWkemOousc+AHpiS55+5f4/4JSgNLOB5juncg+W+foQn+SKL329fJEcg5UnCypmGfgfztOx8F3BTpjSQBNAl9V5W9SO5FXQVqqgLW7hFgmfVrAiejlpScL3qHJ8BZIkXSCJXzSTYyKkv1LwQiXrn6tp9NCISqVtE3RLrQSf6wZ8Et8HeZI05V6eU64CjF8efGwZ6DosctJgymcO+SWH6o5pGpPtlGoPkH0M8Lj7/yICyn00sG9QurHAnjurcveA1rO/cshUIakSqfqVVKWSqvqpaEuq10L4VUbqVazYG2Ez99NBBzFcD/CTUglQ9VIwemNvbKOKB3PqlIzFVR24A7RZdUlllHsepowiSf6pCdI9qSqhXtQ0OaCc1ET5UnOkqZJmS7rxT83kYoEF2w7w0OMFiRFmvV0EbUOOrSJVfV3KIqchJpZ3r8HKfbPeo0j5PKjxJKuem2YNthnpNiyU44Ao4ydc6R8Hugp0EKbc12Gc+PJm7qns+Psh/mVSfY7DHMdJwYz9h1T12rB8zgPO2548ahMvAw9jxrmBwIPY2lM6UziGXFYgAHbFtWvmQmIgGkeVcSUWzu7ioGOJbpl3AIV8TgMmcmDQ+eGYb43WWMMOwVaiT3fr+2dQWgfzuTEd+9yaWkF92mCfapFQhIVC253o7IwrsO3rLaOcvw4LqRZpHfd4zMaQCzyArf3uwzb7UDN8U8he4wt5mlDz8xsE1pQByC2gE3DPqdAuDljfkbacyK88zUnAEYxhIjewjslAE0RPzOwIRhjsgZnHJwPnAHA4uczC1q+P/hrmTw3mNoWiOUGBvUjlaa7kAeqxmUfZwgXg1v9kbGv+eowM9RRGUioP89ya+dv8IPeYyrmmokAcMew42Ba2zK4YD2+a2U9pBUxxHKcfRgQIJtW1co+VgaTnsdG/wxpUm2BvsVcw48Kp2AM0m8CD8wqbGQSlIemaY4/zBxgxbltxEEblC8a9bp0aAn6/d83dc9dgLJtkjJh3T9B1WzCD6yEEeMQOAQZkYwJurQowxXApoTFF0zHuuR+rsHiZEzHlchnGmY6k3IXF5xxI5NiWqzFDaTiDxI9Z7j0UYEwjfz3nU3E8zahYb4bDO7EXhx9TsXs9AWOdXANcyn9J+3979x4jVXnGcfz7zLIgqHhDKBcvKJBA4wWE1Qa1QhCUmKAxEUxtsbHRNtpqk9pIbSqmiSk2ba2kaaORoMZKTVVKW22rRrFpItSiclG5lW0dynKRm6nNIrtP/3jOuOOws8sOs3t2z/w+ycnOnjkM77PvzDPn8pz3fe/7wJnAII5jKBOJMswtLGPqt5oYvxH2//kUfsV0YmLAu4EXMT5gOnAtG/iY+BJ7i0jsLcDSD2Hfh5FkS2esPR+4mb0s5Y5kzQ95lc38ix2M4UwWJ2vvJg6RryH6dC/xnl1DXBAfSLwfVyYxDU3+fyMGFdtJ3L5xAzGVn2RDl5O7u6+DtjIBM2skTr3sMbMVwB1mtgy4GDjg7mnPE1uxOmLPtz9RijiO2Hv+iNhTfZXYgzyfuD/peOJDuoyo0LieymtNS2sujEje5XxI7K2PJcrUfl7y/GwiwZcOxzqKtrlYIRLMfuKIod1hhxMtxN9hD5GYh3ewLUTx4EvQbpX7DuKqfLnkvr7o8VLiiKajth2tPDHJeFPJ+huIfi2UNTqPArfBRWfGf85g6lrmcs1vwclzwvSPOXk8fG73/5i7pom2wsznaWI1DdQxg30MJpI7xBf0i0Tc4yn9UhwLHKA/u+hHC79gT7J+OfAfxvMW805fw9emwvLl0d8ziZ2KIcTedaFEbTowIYllZbKu8L422r7A+1N0k5tkwtGUQj5NzFs7hPiSv8/dHyt6vpG25G7Ejt9VxNHeV9297Ai2Ra/RK/fcy5lCnGa4hThNsIj4oIwh9rYLc3Wu5bN33NUqJ/5Wb8Jniv8qMYD40vwNHMNJr45dTHxhP5r8vhV4jueZdv80TrrxpLZJQM8DfAM33XYzq8auZ8pbA7j//hbiDq+YAvo1YAOncRyDuYJtXE3c9ZwjPkxXEhNCF3+ZD+LLHGYzh3iDfpzF2ck9n42cwWHyLAAeaIDdi2HoxXEn6gDitOErJbF8lzjieoi45Uoyp1bmUO25pR58cvJ4CvhKOr/Ap6XvLa3ESIoGvpjF3kyzHxp3yL3ZYzA5d/cZM9xzU/0Fvu71XO/1TPD65D3S3sXHffVRvTI/eb6wbeH52eAXkPN66nwMZ3kzzd5Ms49mlNcTMzG1EOWl9UXLk0mbW4jXLywN7bRBS2YWDRxW7WUYUblSB76F3jsnpJZjXw6DTySGjM6R8/GMdx9YlNxbWtwXfM9bk22Ll1s5cgCs3EE8d148np9s91HJNotY5Id51reC58h5jpw3Jtu2gL8GPrTk/yq8B9cQXxqFJe3PipZuXZTcq7lMAv8DUTfcD/wF8P2kn4S0dN+yk6gLz4M30c+d0e4jWt1HuPvf3P1Ao3v+x+553PM59/w293ze9171J7+PGz/z/tk0fJPn++V9LnN9EFFSOZzi99hdPpjrfMSsU31yHs834vlc3KTm4A+DX0PUpbcSwxY3JK8zAvx0evbzoCXVRaNCVtOVxDnfwcQF1MlEVcJlZbb/GXHudQwxB+ZgjqwqceIccl07z0lvlOPTauAG4NufwLydtF3+nQUYrDnItp3/5v2isR1nMpM6cqxjPfl2x3w8FzgIW3cz8I9whRPlWokHiPPrDcnv+4jRLkurbaQmaFTIaioM7tUKLCQ+5nOJqof+RGnhAGAqUUXye+LDN5KYyHgQ7Sf31uS1lNz7oBlE2UGx2ynUrXbNE8TAOO/C/l9HuWKxlbRV0kvNU3LvKQOJaoiBwE3EF8GFdF4q2NOcSBLFf/h+RC4qFN5NJI4yjulmoVr2FF3u+L8Ch+/k08HYm4j7K0TKUHJP00NU9gGtJ077HNfZhhVoJW5gOlS07hTgC8R48hBHHBfRdoOWHJtWOh8SdxyVj+QpNUnJvS/6InHD+pcq/Pc6vdNzjuYNvI8j7zoWOUZK7n1RjrjA2tW7XEcT1wB+UPUWSTkLiYvqHXF00VOqTsm9lhRm8xtWxdc8mzhNk0WtRMVTueEPjsZOYHd1miPSFZpmr5YcIhJNNZPNB8QQAlnkRDWTJmOWLNGeu4hI31V2z11VbiIiGaTkLiKSQUruIiIZ1GlyN7MlZrbLzNaXrP+mmb1vZhvM7MGi9QvMbIuZbTSzWd3RaBER6djRVMssJSbg+HSsfzObRkySfYG7N5vZ0GT9BGI6x88DI4CXzWycu3fXvAoiItKOTvfc3f11YlrGYt8AfuTuzck2hWko5wDLPKYy2EbMt9uAiIj0qErPuY8DLjOzVWa20symJOtHEiXRBXnKT3gvIiLdpNKbmPoR8+leQkwp+oyZndOVFzCzW4k5nUVEpMoqTe554DmPO6BWm1krMYH2dmKwwYJRybojuPsjwCOgm5hERKqt0tMyy4FpAGY2jhjOZA+wAphnZgPMbDQwFlhdhXaKiEgXdLrnbmZPE3PMDDGzPHAfsARYkpRHHgLmJ3vxG8zsGeBd4DBwuyplRER6nsaWERHpuzS2jIhILVFyFxHJICV3EZEMUnIXEckgJXcRkQxSchcRySAldxGRDFJyFxHJICV3EZEMUnIXEcmgSkeFrLY9wH+Tn7VgCLURa63ECYo1i/pCnGeVe6JXjC0DYGZvlhsjIWtqJdZaiRMUaxb19Th1WkZEJIOU3EVEMqg3JfdH0m5AD6qVWGslTlCsWdSn4+w159xFRKR6etOeu4iIVEnqyd3MrjKzjWa2xczuSbs91WZmjWa2zszeNrM3k3WnmtlLZrY5+XlK2u2shJktMbNdyXSLhXXtxmbh4aSf15rZpPRa3nVlYl1oZtuTvn3bzGYXPbcgiXWjmc1Kp9VdZ2ZnmNmrZvaumW0wszuT9Znr1w5izUa/untqC1AHbAXOISbZfgeYkGabuiHGRmBIyboHgXuSx/cAi9JuZ4WxXQ5MAtZ3FhswG3gRMOASYFXa7a9CrAuB77Sz7YTkvTwAGJ28x+vSjuEo4xwOTEoenwhsSuLJXL92EGsm+jXtPfcGYIu7/9PdDwHLgDkpt6knzAEeTx4/DlybXlMq5+6vA3tLVpeLbQ7whIc3gJPNbHiPNLQKysRazhxgmbs3u/s2YAvxXu/13H2Hu69JHn8EvAeMJIP92kGs5fSpfk07uY8EPij6PU/Hf9y+yIG/mNk/zOzWZN0wd9+RPG4ChqXTtG5RLras9vUdyemIJUWn1zIRq5mdDUwEVpHxfi2JFTLQr2kn91pwqbtPAq4Gbjezy4uf9Djey2TJUpZjS/wSOBe4ENgB/CTV1lSRmZ0APAvc5e4Hi5/LWr+2E2sm+jXt5L4dOKPo91HJusxw9+3Jz13A88Rh3M7CoWvyc1d6Lay6crFlrq/dfae7t7h7K/AobYfofTpWM6snkt1T7v5csjqT/dperFnp17ST+9+BsWY22sz6A/OAFSm3qWrM7HgzO7HwGJgJrCdinJ9sNh/4XTot7BblYlsBfCWprrgEOFB0mN8nlZxbvo7oW4hY55nZADMbDYwFVvd0+yphZgY8Brzn7j8teipz/Vou1sz0a9pXdImr7ZuIK8/3pt2eKsd2DnF1/R1gQyE+4DTgFWAz8DJwatptrTC+p4nD1k+I84+3lIuNqKb4RdLP64DJabe/CrE+mcSylvjgDy/a/t4k1o3A1Wm3vwtxXkqcclkLvJ0ss7PYrx3Emol+1R2qIiIZlPZpGRER6QZK7iIiGaTkLiKSQUruIiIZpOQuIpJBSu4iIhmk5C4ikkFK7iIiGfR/PXF82MLFAiEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 指定relu激活函数的一层卷积网络\n",
    "model1 = Sequential()\n",
    "model1.add(layers.Conv2D(3,(3,3),activation='relu',input_shape=car.shape))\n",
    "# 一层卷积运算\n",
    "conv_car1 = model1.predict(car_batch)\n",
    "# 对特征图进行可视化\n",
    "visualize_car(conv_car1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00dc25c4",
   "metadata": {},
   "source": [
    "### 11.3.5 池化层TensorFlow实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b3361286",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(object = car,pool = False):\n",
    "    # 创建一层卷积运算\n",
    "    model = Sequential()\n",
    "    model.add(layers.Conv2D(3,(3,3),activation='relu',input_shape=car.shape))\n",
    "    # 如果pool为TRUE,则增加一个池化层\n",
    "    if(pool):\n",
    "        model.add(layers.MaxPool2D(pool_size=(8,8)))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d93bfc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_iamge(model,car=car):\n",
    "    car_batch = np.expand_dims(car,axis=0)\n",
    "    # 对输入数据进行卷积运算或卷积池化运算\n",
    "    conv_car = model.predict(car_batch)\n",
    "    car = np.squeeze(conv_car,axis=0)\n",
    "    print(car.shape)\n",
    "    # 绘制输出特征图的图像\n",
    "    plt.imshow(car)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9d5575d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(173, 285, 3)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21, 35, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'pool=True')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "car = tf.io.read_file('data/car.jpg') # 读取本地图像\n",
    "car = tf.image.decode_jpeg(car,channels=3) # 将JPEG编码图像解码为unit8张量\n",
    "plt.subplot(2,2,1) \n",
    "visualize_iamge(build_model())\n",
    "plt.title('pool=False')\n",
    "plt.subplot(2,2,2)\n",
    "visualize_iamge(build_model(pool=True))\n",
    "plt.title('pool=True')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba26bee0",
   "metadata": {},
   "source": [
    "### 11.3.7 案例：使用卷积神经网络实现手写数字识别"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e40117f",
   "metadata": {},
   "source": [
    "#### 1、 导入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cc7ddd1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f3076f7",
   "metadata": {},
   "source": [
    "#### 2、加载MNIST数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ffd6b112",
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8843191e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_images: (60000, 28, 28)\n",
      "test_images: (10000, 28, 28)\n",
      "train_labels: (60000,)\n",
      "test_labels: (10000,)\n"
     ]
    }
   ],
   "source": [
    "# 查看数据形状\n",
    "print('train_images:',train_images.shape)\n",
    "print('test_images:',test_images.shape)\n",
    "print('train_labels:',train_labels.shape)\n",
    "print('test_labels:',test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4789914b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对数字图像进行可视化\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "for i in range(14):\n",
    "    ax = fig.add_subplot(7,7,i+1)\n",
    "    ax.imshow(train_images[i],cmap='gray')\n",
    "    plt.tight_layout()\n",
    "    ax.set_title(\"数字: {}\".format(train_labels[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc7f5106",
   "metadata": {},
   "source": [
    "#### 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b13e72b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255\n",
    "test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255\n",
    "train_labels = to_categorical(train_labels)\n",
    "test_labels = to_categorical(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62161ca4",
   "metadata": {},
   "source": [
    "#### 构建CNN模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7773f507",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "170f7303",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 3, 3, 64)          36928     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 55,744\n",
      "Trainable params: 55,744\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b2c718",
   "metadata": {},
   "source": [
    "#### 添加全连接层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "826d5fab",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(64, activation='relu'))\n",
    "model.add(layers.Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7891b059",
   "metadata": {},
   "source": [
    "#### 编译模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f37ab0c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d24d8677",
   "metadata": {},
   "source": [
    "#### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "54be7fed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "750/750 [==============================] - 29s 36ms/step - loss: 0.2104 - accuracy: 0.9362 - val_loss: 0.0665 - val_accuracy: 0.9812\n",
      "Epoch 2/5\n",
      "750/750 [==============================] - 26s 35ms/step - loss: 0.0607 - accuracy: 0.9815 - val_loss: 0.0532 - val_accuracy: 0.9850\n",
      "Epoch 3/5\n",
      "750/750 [==============================] - 26s 35ms/step - loss: 0.0421 - accuracy: 0.9870 - val_loss: 0.0500 - val_accuracy: 0.9857\n",
      "Epoch 4/5\n",
      "750/750 [==============================] - 25s 34ms/step - loss: 0.0321 - accuracy: 0.9897 - val_loss: 0.0396 - val_accuracy: 0.9877\n",
      "Epoch 5/5\n",
      "750/750 [==============================] - 26s 35ms/step - loss: 0.0272 - accuracy: 0.9915 - val_loss: 0.0430 - val_accuracy: 0.9877\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x258c9328d90>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b82be90c",
   "metadata": {},
   "source": [
    "#### 评估模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "67f48071",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 2s 6ms/step - loss: 0.0327 - accuracy: 0.9898\n",
      "Test accuracy: 0.989799976348877\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "print(f'Test accuracy: {test_acc}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1129c9ad",
   "metadata": {},
   "source": [
    "#### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "05115999",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "test_labels_pred = np.argmax(model.predict(test_images), axis=-1) # 预测测试集的标签\n",
    "test_labels = np.argmax(test_labels,axis=-1) # 对one-hot编码的标签转换为实际数字\n",
    "# 查看混淆矩阵\n",
    "confusion_mtx = confusion_matrix(test_labels, test_labels_pred) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2e468a78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JGDWkJ6fe9h5HXfcWN57TlVopyVHH7tLD+tJhl9O3X38A1qxdQ8OGjQLThvB6ts/5fDbvvvM2fbN7cuF5gyko2NXVNBbC7tkeDdY9d4iq8sbYMaSkpJCcHH1ycalfWKicO2oCf272uq892+/JmzO9X8TZS1bRqXVjNublc9adHxS9p25qCpvytrIxbyt/bS3kr62FpNaK/t5cZXhYz/7sM9bn5NC1W7dAdcPq2d65y6FMmjaDaTNmkVkvk4kTPij/TVEQds/2SIkmYVbppCkimSIyQUSmiMg4EakZkC4PPPQI3Q7vzgfj3w9CMjD9P/Pyyd28Y7wvrXYKK//YCEDu5nwa10tlzYY88gt22Clv2JTPhs1/cXqv1lx23MGsWb+Z9Rv/ijpu1x7W69at4/+uvpLHn3wmUF0Ir2d7+/YH07RpUwDatDmAH74PtqUWds/2aLCk6XEOcJ+q9gd+A46OVfDee+7i5RdfAGD9+vXUq1cvVkmn+hvztha1GtNTa5KUVPoPfNDt48ndnM8VJx3CiFfmVOhaLj2s8/PzGXzWIG4fPpKWLVsGprudsHq2X3zBeXy1cCGFhYW8+87btD+4Q2DaUL082y1pAqr6qKpO8Q8bAjH73V48ZCivvPwi/Xr3YlthIf36D4hV0qn+/O9/p3vbZgC0b9WA5atLt3/PLyhk3Z9bWPzTWj75emWFruXSw/q5Z55m/pfzuGvkcAb0zeb1sWMC04bwerZff+PNDLnwPLp16chhXbvRp2+/wLShmnm2h+xGkNPJ7SJyOJClqruYKkfre56VlcX4iVPKPa+iBK3/8rQljLvtBI5o14wD96rPnKVld39uOfdwrnp4eoWv5dLDeuhlwxh62bDA9EoSVs/2g9q1Y86XCwPTK0l18mxPlBZkpDiz8BWR+sBk4FRVXb67c11a+Lpmdxt2NK2fRveDmjFl3vKdxjsjJZoNO6qT/7Zr7UTZsCPRPNtdWPjWatJam58T+aY3P943MO4Wvk6Spn/j5wPgrmLd9DKpqkkzVmyXo/iQKEkz0XCRNGs3aa3NB0f+Hfrhv/FPmq5uBF0MdAZuFJEZInKGo+sYhhFqwjflyMmYpqo+BjzmQtswjKpFWTNLEhXb5cgwjPghkCANyIixpGkYRtwQwtfSDNUySsMwqh4ikT92ryNZIvKBiMwSkcf9sqdF5FMRuanYebuURYMlTcMw4kqAN4LOBV5S1Z5AXRH5F5Csqt2BZiLSWkROKVkWbbzWPTcMI34EO6b5B9BGROoBLYANwFj/tQ+BHkDHUsqi2jjAWpqGYcQNIeqWZgMRmVvsMbSY3MdAa+Aq4BugFrDCfy0XaAyklVIWFdbSjBGXE9CzThvtTBsg542h5Z9UQcI8QTxR5gNWD6Kef7l2N5PbRwCXqWquiPwDGA486b+WjtdI3AikliiLCmtpGoYRV4K6EQTUAdqLSDLQFRiF1/0G6AAsA+aVUhYV1tI0DCOuBNiyHwk8C7QEPgPuB2aJSDPgGKAboKWURYUlTcMw4keAN4JUdQ5w0E7yItlAf+BuVd1QVlk0WNI0DCNuuJ7crqo57LhbXmZZNFjSNAwjroTtxluokuZll1zMN98s4ehjBnLdDRWazB8X/Q0bNnDeOWdSUFBAWno6L70yhpo1o7dNatmoLvcPPYKMOinM/W4Nj43/eqfj656dzSVHH8hpPfYFIDOtJl98u4YrH5sVU/yu6qWgoIC2bfalVat9APjv/Q8GboXr+ndm9erVnHjs0cyeOz9Q3aB+Z3aH67qJlJDlzPDcPQ+zpWlQ3uHDzz+MUWO/pN8N77HnHmnMvPuknY57tmvKkxOXcNRN73PUTe/z6f9+45nJS2KK3WW9uPYOrwyb2uv/dS15W/IC13XtN58wFr5iHkFFiEh9EekvIg2C0AuzpWlQ3uH7Nctk/o9rPZ0NeWz+a+tOx5l1drREmtWvQ6N6qcz/YW1MsbusF9fe4a5/Z2ZM/5C0tDQaNw5+V3XXfvMJY+FLoFOOKgVXFr5NgfHAYcB0EWkYq2ZVsDSN1Tt83Kc/ceMZnRl46F7079iC56Yu3el4+lcris69dOBBjJ7wv5hjdlkvrr3DXcaen5/PiDtv544RowLTLA1XfvOJY+FrmxBv5yDgGlWdLSJZQCdgUiyCYbc03e4d/srYNyuscdfr8+l+YGOuPqkDL03/dpfjTVu8lpoIHNmuGf956YuY43ZZL+3bH0ytWrUAN97hLmO/9+5RXDrsb4FbSRcniN+ZskgsC9+4XbpCuLLwneonzF54rc3PYtUMs6VpkN7hC3/6gxYN03nwna9KPQY4om1TvvguZtdkwG29uPYOdxn7h9Om8sRjjzCgbzZfLVzAsKFDAtMG937ziWThay1NH/E+4RnAVqCwlNejsvANs6Vpce/wu0YO55JLh3H6oIrZJl1zUgcefHcRefmFpR4D9O/YnI+/XhVI7C7r5fobb+bC885BVRl43PGBe4e7jH3q9JlFzwf0zeax0U8Fpg3B/s6URsJY+CbQWGWkOLPwLbqAyB3AYlUdU9Y5kbpRJpqlqWsSZcMOs8GtmiSChW/dFgfoIVdH/gfn42t7xt2N0klLU0T+DaxS1ReAesD6IHSzsrI4zb/j5wLX+mElzPUS5thdkyh1kyjd7khxNeVoNHCuiMwEkoHJjq5jGEbISUqSiB+JgCsL3xy8BfGGYRhlE8IxzVAtozQMo2oh0W9CHHcsaRqGEVdCljMtaRqGEV+SQpY1LWkahhFXQpYzLWkahhE/RMI35ciSpmEYcSVBZhJFjCVNwzDiirU0jcBw6UsOUP/MZ5xpr3vtImfaRtUiZDnTkqZhGPFD8OZqhglLmoZhxA8RkkM2qGlJ0zCMuGLdc8MwjAgRwje5PTRulIZ7WjZK563r+zPljoGMPO8w6qXVZNwN3vGDQ7sXndcoszZT7hgYx0iNIFi9ejV9s3vGO4yqZawmIkkicmyx4/4icrT7sErnsksuJrtnd0aNuDN0+mGI/c7BhzLyjQX0v/kD9tyjDmcduR+vzvyB/jd/QHpqCp323YN6aTV58opepNUKrpNS3es9Hto5OTlcctH5bN60KXDtaAmb3cVuk6aqbgOuEpE9/aJTgKJdk0XkQ4ex7USYfc/DEnvrphks+OkPANZs2ELupnz2b5ZJZp2aNN8jjV/WbKJwm3Lu/dPJzduaULFXtrZrfdexJycn8+IrY6ibkRGobrRE08pMkJxZbkszEzgYOE5EkoGBwEIRGSkibYBKs7ALs+95WGIfN3sZN57ekYGdW9C/457M/HoV+zbN4PKBbfl2xQZyNv3Fn3lbyd0cTMIMMvbK1nat7zr2jIwMMjMzA9WsKEkiET8SgfLGNF8GfgOeA84F3gauAj4AbgZ2ax8oIo1FZH7MURJu3/OwxH7XmwuZNP9XLui3Py/P+J7bzunCVaM/ZeQbC1i6Yj3n9d4/yLABq/d4aCcaEsUjESgvaZ6IlzgfBo4H/gkUAD8BzwDlJcR7gdQYYwTC7Xsepti/WvYHLRqk8+B7i6lTswYH7ZVFUpJwaOuGKMGbpVm9V752olGlxjSBvfBak+8BKXh+PwBDij0vFRHpA2zCa6nGTJh9z8MU+zUntOfB9xaTl1/IveMW8vBlR/Db84PJSq/F2I9/DCjiHVi9V752IuFNOYr8kQiUdwt0L6APMBd4HngWqAnch9cKLRURqQncApyE16Uv7Zxq43septjvHLuj8zD3+7V0uWZcqecd/Z8JFb5GcazeK1+7OJOnzXCiGzEJ1IKMlPJamv8GfgQeVdU38VqbD+AlzJ92877rgEdUdX1ZJ6jqaFXtoqpdGjZoWG6gGRkZTJo2g8O6dmPilOmBD2K71A9z7K6xeq987UQjbG6Uorr7cSoRuRQ4UlXPFpFs4BpVPdF/7RNVPaKU98xkx531Q4A3VHVIWdfo3LmLfvL53Ap9AKPi2C5HRjQc0bUL8+bNDTRzNdjnID1u+KsRn//82R3mqWqX3Z0jIo8CE1T1PRF5GjgQ+EBV7/Rf36UsGsqdoayqT4jIdP9wFlC72Gu7JEy/vFexDzBjdwnTMIzqTZDdcxHpCTTxE+YpQLKqdheRR0WkNdC+ZJmqRjUJNqJllKr6rYgMVtVCVZ0oIr1EpMwxzRLvzY4mIMMwqhdBTTkSkRTgSWCZn5+ygbH+yx8CPcooi4ryJrdPF5F0//Aif1nlS8B/gD+ivZhhGEZxRKKe3N5AROYWexTfqfs84H/A3cBhwN+AFf5ruUBjIK2UsqiIpHu+0X+6Da9r/izwGTiYtGcYRrUjyt752t2MaXYERqvqb37jrjs75omn4zUSN5ZSFhXlJc3iiXFv4CX/+d+AWiKySVUHRXtRwzCM7QQ4pvk9sI//vAtezuoBzMabb74U+LWUsqgoL2kW/zQ/quopIjIMeF1V14qIu9uvhmFUCwK8D/Q08IyInIk3PTIbeFdEmgHHAN3wGoKzSpRFRXlJ8xMRqY23dHL72GYNYKyILFFVm1diGEaFEYLbiENV/wRO30nfmybZH7hbVTeUVRYNu02aqnqTiDQEHgcuE5H3gcmq2kdEDor2YoZhGDvheMs3Vc1hx93yMsuiYbdJU0TG4zVnDwaGA22AfD9TJ4lITVW1LbxDissJ6PteVfryy6D44cGTneoblUfYllGW1z0/BagHvI63hvwkYDNwPbAKb9zAMAyjQgiQHLKkWd7O7X/h3aIfrapPq+rxeNvEnaOqBaqaVxlBGoZRdQnbLkflzlFS1U3AqyJyjn88G3hdRP7lOjjDMKo+VSppisjbIlIDb2L7EBGpLSJPAu8CayojQMMwqi6e90/V2oQ4ze+Gq39uEjAFb27TGNfBGdWTenVS6HlAQ7LSasY7FKMSqFItTXZeEdQe72bQEOBNYHxlulFu2LCBE487hmOP7s+g004mPz8/8GuE1Y7Vdd0EFXtmagovXH44b/2jJ6POOmSXY4BGGbV44fLudNw7i9ev7kH99NgTp0t/b9fe4a70K+P7FClVyo2Sne+uL1DVAXgtzONVtTfwg7PISvDaKy9z1dX/YPzEKTRp3ITJkyYGqh9mO1aXdRNk7Kd2bcFbc37hlPtmkVarBhdm77PT8cF71aNN0wxufeMrHpz4LR8t+Z32LerFFL9Lf2/X3uEu9V1/nyLFs7uoWm6Uz/g7GyUDNXwbi6Z4y5BuBP7uPEKfS4ddTt9+/QFYs3YNDRs2ClQ/zHasLusmyNhzNuWzb+N0MlJTaJaVyop1eSWONzNr6Rq+XJZD1/324JCWWcz7aV1M8bv093btHe5S3/X3KRqSongkAuXFcTme109P4ELgHOBQ4ClgLd7yyl0QkRoi8rOIzPAf7YMKePZnn7E+J4eu3aJeMrpbqoIdq4u6CTL2OT/8QatG6VyUvQ/fr/6TT79bs9PxhmJ+6id0bk5B4TYKt8W2mZZLf2/X3uGV4U3u6vsUDVWtey5428Adg2esVhevdTkMGKOqZQ2EHAy8qqrZ/mNREMGuW7eO/7v6Sh5/Mvh9QsJux+qqboKM/boTDuK6VxfwwISl/PDbRt77Z/ZOx2cc3rLo3BvHLGTuj+vo375JzJ/BKB2X36dIkSi65mHpnu8NnAE0Ar7D26/uFjyztft2s8tRN+BkEflYRF72py3FRH5+PoPPGsTtw0fSsmXL8t8QJWG2Y3VZN0HGnlozmQOaZZAk0LFVFrVSknY6VuDy/q05rWsLADLqpOzU+jSCw/X3KRrC1tIsL5kNAPLx7qJvD7kh3hzN5N28/ws8M7ZVIvIIMBBvbmcR0Vr4PvfM08z/ch53jRzOXSOHc8mlwzh90Bnlvi9SwmzH6rJugoz94Unfct+5nWhevw7zflrHRY/P5u6zOxYdvz33F1KSknh8yGGc1X1vlq7K5aMlvwfyOYydcf19ihQBaiTKXKIIicSN8jtgw/bdkkXkI1U9UkROBg5V1RtKeU8tfwkmInIlUFNV/1vWNRLFjTInJ4dpU6fQo2cvmjQJtlvoUts1FYndNuyoerhwo9xz//Z62aOR/67c0r91uW6Uromk27xMVfsXO97k302/DjiujPe8KCLDgcXAycCI2MKsHLKysjjtdDcb0bvUdk2YYzcSnASatB4pkSRN9ffOzAK+9csGA++qallLKW8HXsFrfb+rqlNjjtQwjCqJlOszmViUmTR9O8zT8O6YH4g37agp3pSjGcD9Zb1XVRfj3UE3DMMoE29ye7yjiI7d3T1vgLclfIGqvoE3rjkImIu3x+ZV7sMzDKOqU2XWnqvqKt8DaIuIHAbUFpHj8G4e3QAcJyLxW0ZgGEaVoKrtcgTedKNvgefwVgRtn938NHCmm7AMw6gObO+eV4mWZjFaArOAXLwbPHX88kl4Y52GYRgVI4qJ7QnS0Cz/7rmqtil+LCL3+OUrRaTSNuwwDKNqkijLIyMl6uWNqjq+2PP4bcJnGEboCePd85jXhBuGYVQcCZ0bpSXNakx5S2hjwfUyxyYXvORM+7fnBjvTNnZGSJyxykixpGkYRvxIoLvikWJJ0zCMuFLlbwQZhmEERRi754liuxER69atY9rUKaxduzaU+oZh7EpV27k9YVi1ahUnn3Asc7+Yw9H9erNmTVkbLCWmflgpKChg/31bclS/3hzVrzeLFwXiXFJEUFayLRumMeba3nxw8wDuPLtTUXnDjNrMvHPgTueWVpaouLYITgTCNrk9NElzyf++5u577+ff199IvwFHsWD+l6HSd+l77lJ/0aKvGDToTCZNnc6kqdNp1z4wjzwgOCvZW8/sxD3jFjHwjsk0q1+HHgc2BuDOsztRu2byTueWVlZRXP5cXVsEu/6djASh6rlRJgx9+vaja7dufDxrJnO/mEPXboeHRt+177lL/Tmfz+bdd96mb3ZPLjxvMAUFpRqQVpigrGT3a1KXhcs8u9+1uVvISE2hV9vGbPqrgN83bCk6r7SyiuL65+rSwtd17BEjVXPDjoRBVXlj7BhSUlJITg6mpVAZ+q59z13qd+5yKJOmzWDajFlk1stk4oQPAtMuTqxWsu/M+Zl/n9KeozvuSd+DmzFryWr+dfLB3DZmftE5KclJu5TFguufq0sLX9exR4NE8YhIT6SxiMz3nz8tIp+KyE3FXt+lLBqcJk0ReVREjg9QjwceeoRuh3fng/HvByXrXN+177lL/fbtD6Zp06YAtGlzAD98H3yLJAgr2XvfWczUhSs5L3s/Xp31I8OOOoAnpyzdyc3ymuMP2qUsFirLz94FiRK7t4wy8BtB9wKpInIKkKyq3YFmItK6tLJoY3aWNEWkJ9BEVd8LQu/ee+7i5RdfAGD9+vXUq1cvCNlK0Xfte+5S/+ILzuOrhQspLCzk3Xfepv3BHQLThmCtZBctz6H5Hmk8MmEJ2e2acEn/Nrx/Y3/a75XFg0O6lVoWC5XhZ++KRIo9yJamiPQBNgG/AdnAWP+lD4EeZZRFhZOk6VtlPAksE5ETg9C8eMhQXnn5Rfr17sW2wkL69R8QhGyl6Lv2PXepf/2NNzPkwvPo1qUjh3XtRp++/QLThp2tZAf0zeb1sWMqrHXVsW15ZMIS8vILGXjnFI4b7j0W/ZzDVU/NLrUsFlz/XF2SOLELSUmRP4AGIjK32GNokZJITeAWPNNHgDRghf88F2hcRll0EbtYfywiFwPHApcDVwK/qepDJc4p7nve+dsflgceR6KQm5tLv+yeZPfpW+QdHuRYVUX1Xa49dz1onwhrz13/XF1SkdhdWPju27aDjng58nHyMzs1L9PCV0RuAZao6usiMgNYCLyqqrP9bvkBeElypzJVjcot11X3vCMwWlV/A14Cepc8QVVHq2oXVe3SsEFDR2EkBhkZGUyaNoPDunZj4pTpgX+xXOsbpRPmek+k2AO8e94P+JufMA8BjmdH97sDsAyYV0pZVLhaRvk9sI//vAtQdZuREeLaO9y8yeNDmOs9UWIPqumqqr2KNL3EeQIwS0SaAccA3fDse0qWRYWrlubTQG8RmYnXRb/X0XUMwwgzjuZpqmq2qubi3fiZDfRW1Q2llUUbspOWpqr+CZzuQtswjKrD9hVBrlDVHHbcLS+zLBpslyPDMOJKoqz0iRRLmoZhxJVwpUxLmoZhxJmQNTQtaRqGET+8Mc1wZU1LmoZhxBVraRqGYURM4uzIHimWNKsxYbtrWRyXNrtZJzzoTBsg592rnOqHCeueG4ZhREMC2VhEiiVNwzDiiiVNwzCMKBDrnhuGYUSGt3N7vKOIjlB5BIUZ81Q3jNKRKP4lAqFKmq4tR1evXk23Lh0D160MT3WXdRNW++GgtBvVS2Xq3acCUCM5iTdvPZ7p957Oef3bFp3TpkUWY28+rui4T8cWTBp1CjP+ezqDjtw/brGXRVB+80FgvueOqAzL0ev/dS15W/IC13Xtqe6ybsJsPxyEdr30Wjz5jwHUqZ0CwOUndODL736n97WvM/CwvUlPTaFVk0xGXNyDzLSaACQlCaOG9OTU297jqOve4sZzulIrJTp3U9f1HpTffBBYS9MRri1HZ0z/kLS0NBo3bhKoLrj3bHdZN2G2Hw5Cu7BQOXfUBP7c7LXEerbfkzdnegls9pJVdGrdmI15+Zx15w7LhrqpKWzK28rGvK38tbWQv7YWklorutsHrus9KL/5WNk+phnpIxFwZaw2TERm+I8FIvJErJouLUfz8/MZceft3DFiVGCaJXHp2e6ybsJsPxyE9p95+eRu3tF1Taudwso/NgKQuzmfxvVSWbMhj/yCwqJzNmzKZ8Pmvzi9V2suO+5g1qzfzPqNf1V67JEQq9987ETTzkyMrOkkaarqY/7OydnALGB0rJouLUfvvXsUlw77W+C2wMVx6dnusm7CbD/sQntj3taiVmN6as3tDom7MOj28eRuzueKkw5hxCtzor5OZVjsBuE3HzNRtDKrdEtzOyKyJ9BYVefFquXScvTDaVN54rFHGNA3m68WLmDY0CGBaYN7z3aXdRNm+2EX2vO//53ubZsB0L5VA5avzi31vPyCQtb9uYXFP63lk69XRn0d1/UepN98LHjdc4n4kQi4nqf5N+Cx0l4oYeFbrtDxJ55Ev+yerFq5sshyNCimTp9Z9HxA32weG/1UYNrgeaoPPmsQzz7zFAcd1C5wz3aXdeNS27W+C+2Xpy1h3G0ncES7Zhy4V33mLC2723zLuYdz1cPTK3Qd1/Ve3G/+rpHDueTSYZw+6IxArxEpiZEKI8eJ7zmAiCQBn6hquXc9Onfuop98PrdczZycHKZNnUKPnr1o0iT4GzZhxmXduK73RIu9vA07mtZPo/tBzZgyb/lO450RxxThhh2J9vvuwvf8wPYd9dm3I//Dcvh+WWX6nlcWLpPmkcDJqnp1eedGmjQNozKwXY5Kx1XSfO7tGRGf322/enFPmi6750cBM8s9yzCMak2CDFVGjLOkqao3uNI2DKPqELKcaRt2GIYRZ0KWNS1pGoYRNwTbGs4wDCNyEmgjjkixpGkYRlwJWc60pGkYRjyR0Bn8WdI0DCOuhCxnWtI0DCN+CNY9rxCKt3WaC8LW9Dfij+sVOw0HP+9Me81L5zvTdkbIvqIJkTQNw6i+2JQjwzCMKAhbZzA0dheGYVRNJIrHbnVEMkVkgohMEZFxIlJTRJ4WkU9F5KZi5+1SFg2WNA3DiB/RZMzyW6TnAPepan/gN+BMIFlVuwPNRKS1iJxSsizakEOXNP9+5eWMf/+9eIcRFYlkl5qIrF69mr7ZPUOnH+afayLFHqVHUAMRmVvsMXS7jqo+qqpT/MOGwGBgrH/8IdADyC6lLCpClTQ/+XgWq39bzbHHHe9E35XPdGXYpSa6d3hZ5OTkcMlF57N506bAtV3rB/VzbdkwnTf+1ZdJtx7NiME7toq876KuHNOpedHxI5d2Z+rtx/DPkw9OmNhjRYja93ytqnYp9tjFf0xEDgeygF+AFX5xLtAYSCulLCpCkzS3bt3K3y4bSsu9W/Leu+8Eru/SZ9q1XWqie4fvjuTkZF58ZQx1MzIC1a0M/aB+rref3Zm73lrIUbdOpNkedejRtjHdD2hE43qpTPjyVwBOOHQvkpOEfrdMoGlWKvs2qZsQsQdBcL1zEJH6wEPARcBGINV/KR0v35VWFhWhSZovv/QCBxzYlmv+71/M/WIOjz3yUKD6rn2mwZ1daqJ7h++OjIwMMjMzA9WsTH2I/ee6X9MMFvy0DoA1G7aQmVqThy7pzs9rNnJs5xYA9GjbhLc+WwbAR1//xuFtom4gOYk9CEQk4kc5OjXxut7Xq+pyYB47ut8dgGVllEWFK9/zLBH5QERmicjjQWguXDCfi4ZcQpMmTTjr7MF8NGNGELJFuPaZdmmXmuje4VWZIH6ub3++nOtP68AxnZrTr8Oe7NUonW9WrOf+dxfTeb8GXHrUAaTVrsHKdZsB+DNvK40yaydE7EEQZfd8d1wMdAZuFJEZeI3Tc0XkPmAQMB54u5SyqHDV0jwXeElVewJ1RSRmT499992Pn376EYAv581lr5blO1hGg0ufadd2qWHzDq8qBPVzvWfcV0xZsILz+7TmlZk/sG+TDJ6d9i2/b9jCmFk/0uugJmzcspXUmskApNeqUabfemXHHgRBdc9V9TFVzVLVbP/xPN6Nn9lAb1XdoKq5JcuijddV0vwDaCMi9YAWwM+xCp5/4cXMnDGD/n2OZPQTj/H3a66NVXInXPpMF7dLHdA3m9fHjglMG8LnHV5VCPLn+tWydTTfI42Hx3/Nj7/lsncjb8yy47578PPaTSz48Q8OP8DrkrdrmcXyNRsTJvaYCXJQswSqmqOqY1X1t92VRRWuizXfItISGAl8AzQH/qaqW0ucU9z3vPPS75cFHod/nYjOy83NpV92T7L79C3ymXY9FhYULmMPc70kKqWtPb/htA78uPpPXpv1I+m1a/DoZUfQKDOVlGRh8P0fsXHLVibdejQfLV5F/0P2pM9NH5Cbt3UXHZdrz124Ubbv0EnfmvxJxOfv36RO3N0oXSXNl4FhqporIv8ANpY2NWA7nTp30U9mfxF4HH4sEZ+baD7T0ZBo3uFG2VR0w456aTXp3b4pnyxZze8btpR6TuiS5iGddFwUSbN14/gnTVdrz+sA7UVkNtAVmOroOoGSlZXFaf6d4rDhMvYw10tVYv2mfMbNXh7vMAInZEvPnY1pjgRGAxuA+sCrjq5jGEbYcTim6QInLU1VnQMc5ELbMIyqRNHyyNBgW8MZhhFXwrY1nCVNwzDiRgL1uiPGkqZhGHElbJY0ljQNw4grIcuZljQNw4gvIcuZljQNw4gjkW3EkVAkRNL0NiINWc1VAbZtc2ObDMS8oUQ8cWUnvR2Xq3ayznjamfZfP651pByu35WESJqGYVRPtu/cHiYsaRqGEVdCljMtaRqGEV+spWkYhhEFtozSMAwjGsKVM8NjrLYdlx7ZYbXBdaW/evVq+vfpVXT8zZIlDDr1pMD0wb3/tst6H/3EYxzVrzdH9etN1y4dueLySwPTDqpeWjZK560bBjD1jmMZdf5h1EurybgbveMHh3bf6dwHLunOwC4tggg/KkK2yVG4kqZLD+sw2+C60M/JyWHoxRewya/rH3/4gRuv/xe5uVFbquwWl/7brut96KXDmDR1OpOmTueIHj24aMjQwLSDqpfh5x7KqNfn0+/m8ey5RxpnH7kfr330Pf1uHk/d1BQ67dsAgCMObEyTeql8MPeXwD5DJIhAkkjEj0QgVEnTpYd1mG1wXegnJyfzwsuvkeHXdXrdurwy5o2YdUvi0n+7MmyZAVasWMHvq3+nU6fOgWkGVS/7Nc1k/k9/eDob8tiwOZ/We2aSWacmzRuk88uajdRIFh4Z1oPlazZy3KHBGhZGRMiamq4sfFuJyHjfwve/Qem69LAOsw2uC/2Sdd2oUSNq1aoVs25ZuPDfriz74Scee4RLLr3MiXas9TLus2XcOKgjA7u0oH/H5sz6+jf2a5rJ5ce2ZemK9eRs+otzslvzzS/rue/tr+jSuiHDjmkb8KfYPSHLmc5amncBd/gWvs1FJNvRdQIjzDa4YbfZdeW/XRn1sm3bNmZ+NIMjs3sHrh1Evdz15gImf/krF/Rtw0vTv+e2sztz5ROfMPL1BXy7YgPn9dmfQ1rtwdNTvmH1+jxe/eh7jmzXNMBPUT4B+p5XCq6S5v7Al/7z34GEty8Msw1umG12XfpvV0a9fPLxLA497LDAdYOsl4XL/qBFg3QefG8RdWrVoN1eWSQlCYe2bogq/LAql1aNvWGYTvs15OcY7YGjQ6L6lwi4mnL0BvAf31jtaOD6kieUsPB1FEbkHH/iSfTL7smqlSuLrGrDoF0Z+i4p7r9918jhXHLpME4fdEYg2pVRL1MmT+KIHr3KPzFKgqyXa05sz4PvLyYvv5B73lrIE1f0Yq8G6Xz+7e+M/fgHBOGJv/Xk9B77kJKcxNn3Tgv405RNGJdROrHwBRCRHsA/gS9UdbfzPTp37qKffD7XSRzREGYb3IroV4cNOypSL6437HC5OY3TDTs+vINtOcsCDb5jpy764cefR3x+/bQaVdbCF2ABsBdwlsNrBEqYbXDNZrd0rF4Sn7C1NF0mzX8C96nqZofXMAwj5CTKWGWkOEuaqvofV9qGYVQREuiueKTY2nPDMOJGIs2/jBRLmoZhxJWwuTZY0jQMI66ELGda0jQMI76ELGeGa8MOwzCqIAEuPheRp0XkUxG5yVW4ljQNw4grQS2jFJFTgGRV7Q40E5HWLuK1pGkYRtzYvowyoA07soGx/vMPgR4uYk6IMc0vv5y3NjVFlkd4egPAlQGza32LPT76Fnsw+sHuqAJ8+eW8Sakp0iCKt9QWkeJrrker6mj/eRqwwn+eC+wXRIwlSYikqaoNIz1XROa6XHvqUt9ij4++xR4//fJQ1aMDlNsIpPrP03HUk7buuWEYVYV57OiSdwCWubhIQrQ0DcMwAuBtYJaINAOOAYKzAShGGFuao8s/JWH1Lfb46Fvs8dOvNFQ1F+9m0Gygt6oG6wLo42w/TcMwjKpIGFuahmEYccOSZiUhYduVoBhhjj3MuKx3+5lWnNAkTRFJi3cMFUE8mgDJDrSTRGQPETlERGo70BcROVBVVUQC/V3xY+8vIs58gUWkjkNtEZEHRCR4gyBPv546GjsTkXRX2tWBhE+a/pdrNHC3iJwkIgc7uMa1QWv6uknAM3iD7VeISMTzUSPkMOBm4EF8W5GAk9sRwDQRaa+q24Jqnfgxvga0U9W/gtAs5RrX4NmtuNBOAl4AuuLPCwyybkTkGeBRETnXgfYTwGgRuUJEnEz+ruokfNIELsVrpd2CZwV8tIgMCPga54vIPQFrAowCVgNXAIcCHQPWH4ZnlXwxcJqItFPVQMy9/S/qz8CfwH0i0inAFudrQI6q3i8iw0XkRBFpH4BucbKAM0Skm69/TBCifr08AywFLgQuFJEmAbbcRgI5eH8MLxaRtgFqjwLWA7fjrZbpHJButSIMSfMrYBvebP+3gUVAdxGJeQ6W38XKwjOBaygij8eqWYJ5wHuq+jPwDnBqwPpvAXPxklsScLOIXCkibWIVVo+fgWeB8Xgt/Y54ratYuRfY5Fs8J+H9QTktiA0WirXKPgH64C0T3B84W0QuiVUfqA9MVNU7VfUbvOktdfxrB/F9mg3MB34HJgODReSfInJ4ANo/A2P8uOcA/xaRxgHoVivCkDS/AfKAw/x5V5/i/UIdFKuwnxhygGGqegGwJeDE+TGw2H/+M94XGBHpLCL7BqD/EfCD38W9H7gDaA4EknxEJBmvi/se8B+8P1pDY9XG+8OXB7yjqtcDTwBtgANjFS7WKvsOr76Xq+o9/jUaBaD/h6q+VqwoCbjJfy2IVv5cYE+8IZ2teH+0WhJML+Vz4H4RGYyX6H8FnI0pV1USPmmq6h94v0Bni0g2Xnfxd+BSEUndzVujYYt/rauBjSLyUhCiqrqi2ATbL4ClInIGMALYFID++u1jgqo6WVUX421Y0N1PehUeC/P/oBTi7RrTHTgdL9llQmytKlXNw0vyz/rHvwDfEuwGC8uBCez4A3IA0EtEagYhvr1uVfU+YJ1425LFjF8X9+CNmb6qqt/hJc9eIpIc48/0C+DveBtbLATW4A3tGFEQimWUqrpYRP4f3hf3ckCBp/wvXxD6BSIifqK41h9na6KqvwWh7/+ip+GNz/4EnB2UdrFrXIx3Y6gDMDjAcbCfgEfxhhmOE5EaEHurSlXX+nn9JrybKecA/WKOdod+oYg8BVwiIn3wkuaVqpofkL5u/53B6000F5EUVd0agPY2EVkJPCEiS4GLgJ7+H7FYtRfgDUchIoVAtojUUNWCWLWrC6FaESTetJr98bbh+y5oT3URSQrqRkpp2njd5xdUdakD/QZ4421b/LHIILX3VtVl/vPkIL68xbRb43VHV6rqt0HpFtOvi9dNz1fVFeWdX8FrNAZQ1dUB67bCS/Y/OKqbRnib9q4KWrsqE6qkGXbC/he9WMvKMKotljQNwzCiIOFvBBmGYSQSljQNwzCiwJKmUSYiklJ8apGI1PCX4tXdzXv28RcMGEaVxJKmUYSI9BSRKSLynoiswJvD946I/CEib+NNbu8OTBWRbBF5XUSeE5Ex/moh8KbHdPT1aovIB/7z4SIyo9jjoxLXnuBr7uMn5/EiUq/YJHvDSAhCMU/TqBxUdZaI3AUcDTyjquOAx0VkkqqetP08ERmIt7a7ELgRb45lAxGZjLdia5uIpOPNTVV/astwvOlQ2/zWa3oxvcuADOAVYDrePMLmeKvBFuFtSPKey89uGJFiLU2jJJuBrqo6TrzNLuYAy0XkcRH5Srw1/0eo6vf++Y8D9fBWrRSfOH4y8AbeZPsngK3b58Cq6jbfmgB/nXw23jrxe/BWIB2A5+9yG3CRqlrCNBIGa2kaRYjIOXhry0VEZgATgQ/wNkv5DK/19zVwp4h847+tEM9jeidU9UXx9so8GbgVmCkim4BWeEsck/Basy8AZ/rX/wW4BC8BnwU8GeZ5rUbVxJKmUZxX8brI7wH/A1YC+wBNgLUAqvqniBwPFOCtzEop9rwkJ+FtxDESr3W6zR8bHYzXVS/wl2X2AK7ydc7w/78HuNUfEx2nqpNcfGDDiBZLmkYR27vP/p4Q1wOd8JJmC7xdmrYnxhPxlrMW4I1F/smOBIqvcTheC3QxMANvE+btrcZueHtRngP0x9tP4CngauBN/5wU4Eq87r3dCDISBhvTNErFX1++Ga8r3RD4C/jMv4lzFV6SexlvM95WeImx+EbOvfESL6r6ADvGOMHbM7KZiGSr6gRVPR5vXPRkvH0eh+KNZ3ZX1dtV9QNnH9QwosSSprET/o5M27c9+xqoC0wDXvT/H4J3h/xPvB3Ab8XbWu8yvAR6AFCoqiPwuvciIu2Ab1T1d6Am3i5V/8G7A7+dH4ET8PYgbY23I9Rz7j6pYVQM654bRfh7TX4KvOqPNT6Ml0Avx9u+bSxekhyLt6Hvbar6q4iMAJoCjfHsN+b4kilALX+fz2Ei8iywWVU3ATOLXfdTvFZtcToCTf2dp7IdfFzDqBC2YYdRJiKyZ/Ht1MRzd/wryK3hDCNsWNI0DMOIAhvTNAzDiAJLmoZhGFFgSdMwDCMKLGkahmFEgSVNwzCMKPj/dSupjFRCv/0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 混淆矩阵可视化\n",
    "import itertools\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  #用来正常显示负号\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='混淆矩阵',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('实际标签')\n",
    "    plt.xlabel('预测标签')\n",
    "plot_confusion_matrix(confusion_mtx, classes = range(10))"
   ]
  }
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